subject:(Text Mining)

Queens University

1. Alsadhan, Nasser. Inferring Systemic Functional Language Models .

Degree: Computing, 2014, Queens University

URL: http://hdl.handle.net/1974/12396

► Language production in the brain is a complicated process that is not yet fully understood. The bag-of-words model, which considers the frequencies of each word… (more)

Subjects/Keywords: Text Mining

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APA (6^th Edition):

Alsadhan, N. (2014). Inferring Systemic Functional Language Models . (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/12396

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Alsadhan, Nasser. “Inferring Systemic Functional Language Models .” 2014. Thesis, Queens University. Accessed February 27, 2021. http://hdl.handle.net/1974/12396.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Alsadhan, Nasser. “Inferring Systemic Functional Language Models .” 2014. Web. 27 Feb 2021.

Vancouver:

Alsadhan N. Inferring Systemic Functional Language Models . [Internet] [Thesis]. Queens University; 2014. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1974/12396.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alsadhan N. Inferring Systemic Functional Language Models . [Thesis]. Queens University; 2014. Available from: http://hdl.handle.net/1974/12396

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Queensland University of Technology

2. Shen, Yan. Personalized ontology learning for enhancing text mining effectiveness.

Degree: 2013, Queensland University of Technology

URL: https://eprints.qut.edu.au/63699/

► Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that… (more)

▼ Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves…

Subjects/Keywords: text mining

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Shen, Y. (2013). Personalized ontology learning for enhancing text mining effectiveness. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/63699/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Shen, Yan. “Personalized ontology learning for enhancing text mining effectiveness.” 2013. Thesis, Queensland University of Technology. Accessed February 27, 2021. https://eprints.qut.edu.au/63699/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Shen, Yan. “Personalized ontology learning for enhancing text mining effectiveness.” 2013. Web. 27 Feb 2021.

Vancouver:

Shen Y. Personalized ontology learning for enhancing text mining effectiveness. [Internet] [Thesis]. Queensland University of Technology; 2013. [cited 2021 Feb 27]. Available from: https://eprints.qut.edu.au/63699/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shen Y. Personalized ontology learning for enhancing text mining effectiveness. [Thesis]. Queensland University of Technology; 2013. Available from: https://eprints.qut.edu.au/63699/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Cornell University

3. Haque, Asif-ul. Information And Social System Interaction.

Degree: PhD, Computer Science, 2011, Cornell University

URL: http://hdl.handle.net/1813/30752

► Ever increasing participation has made the interaction between information and social systems not only interesting to observe but essential to quantify and analyze. This dissertation… (more)

Subjects/Keywords: data mining; networks; text mining

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APA (6^th Edition):

Haque, A. (2011). Information And Social System Interaction. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/30752

Chicago Manual of Style (16^th Edition):

Haque, Asif-ul. “Information And Social System Interaction.” 2011. Doctoral Dissertation, Cornell University. Accessed February 27, 2021. http://hdl.handle.net/1813/30752.

MLA Handbook (7^th Edition):

Haque, Asif-ul. “Information And Social System Interaction.” 2011. Web. 27 Feb 2021.

Vancouver:

Haque A. Information And Social System Interaction. [Internet] [Doctoral dissertation]. Cornell University; 2011. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1813/30752.

Council of Science Editors:

Haque A. Information And Social System Interaction. [Doctoral Dissertation]. Cornell University; 2011. Available from: http://hdl.handle.net/1813/30752

Queens University

4. Lamb, Carolyn. Detecting Deception in Interrogation Settings .

Degree: Computing, 2012, Queens University

URL: http://hdl.handle.net/1974/7695

► Bag-of-words deception detection systems outperform humans, but are still not always accurate enough to be useful. In interrogation settings, present models do not take into… (more)

Subjects/Keywords: Data Mining ; Deception ; Text Mining

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APA (6^th Edition):

Lamb, C. (2012). Detecting Deception in Interrogation Settings . (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/7695

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lamb, Carolyn. “Detecting Deception in Interrogation Settings .” 2012. Thesis, Queens University. Accessed February 27, 2021. http://hdl.handle.net/1974/7695.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lamb, Carolyn. “Detecting Deception in Interrogation Settings .” 2012. Web. 27 Feb 2021.

Vancouver:

Lamb C. Detecting Deception in Interrogation Settings . [Internet] [Thesis]. Queens University; 2012. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1974/7695.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lamb C. Detecting Deception in Interrogation Settings . [Thesis]. Queens University; 2012. Available from: http://hdl.handle.net/1974/7695

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

5. Xu, Jinyao. A general tool for interactive analysis of opinions in reviews.

Degree: MS, Computer Science, 2015, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/78669

► Online review data contains useful information about consumers’ opinions about the reviewed products. Understanding reviews is extremely important for a producer, such as a corporation… (more)

Subjects/Keywords: Text Mining; Reviews

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APA (6^th Edition):

Xu, J. (2015). A general tool for interactive analysis of opinions in reviews. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/78669

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Xu, Jinyao. “A general tool for interactive analysis of opinions in reviews.” 2015. Thesis, University of Illinois – Urbana-Champaign. Accessed February 27, 2021. http://hdl.handle.net/2142/78669.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Xu, Jinyao. “A general tool for interactive analysis of opinions in reviews.” 2015. Web. 27 Feb 2021.

Vancouver:

Xu J. A general tool for interactive analysis of opinions in reviews. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2142/78669.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xu J. A general tool for interactive analysis of opinions in reviews. [Thesis]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/78669

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

6. Chen, Yu-ling. The Research on the Detection of Noteworthy Symptom Descriptions.

Degree: Master, Information Management, 2016, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0306116-140216

► The advance of cell phone and technology create a convenient way to connect doctors and patients. Doctors can keep track of patientsâ situations by their… (more)

Subjects/Keywords: Text mining; CKIP; LIBSVM; Classification

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APA (6^th Edition):

Chen, Y. (2016). The Research on the Detection of Noteworthy Symptom Descriptions. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0306116-140216

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Yu-ling. “The Research on the Detection of Noteworthy Symptom Descriptions.” 2016. Thesis, NSYSU. Accessed February 27, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0306116-140216.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Yu-ling. “The Research on the Detection of Noteworthy Symptom Descriptions.” 2016. Web. 27 Feb 2021.

Vancouver:

Chen Y. The Research on the Detection of Noteworthy Symptom Descriptions. [Internet] [Thesis]. NSYSU; 2016. [cited 2021 Feb 27]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0306116-140216.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen Y. The Research on the Detection of Noteworthy Symptom Descriptions. [Thesis]. NSYSU; 2016. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0306116-140216

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Georgia

7. Tarkhadkar, Sagar Santosh. Mutaimpact miner.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/30031

► Manual curation of knowledge from biomedical literature is both expensive and time consuming. Scientific publications in biomedicine have an enormous amount of valuable information on… (more)

Subjects/Keywords: text mining; Natural Language Processing

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APA (6^th Edition):

Tarkhadkar, S. S. (2014). Mutaimpact miner. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/30031

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tarkhadkar, Sagar Santosh. “Mutaimpact miner.” 2014. Thesis, University of Georgia. Accessed February 27, 2021. http://hdl.handle.net/10724/30031.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tarkhadkar, Sagar Santosh. “Mutaimpact miner.” 2014. Web. 27 Feb 2021.

Vancouver:

Tarkhadkar SS. Mutaimpact miner. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10724/30031.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tarkhadkar SS. Mutaimpact miner. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/30031

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

8. Hu, Xiaocheng. DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH.

Degree: 2015, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/25731

► Most of the researches on corporate filling mainly focus on qualitative analysis. This thesis used quantitative method-Bayesian Learning Machine in analyzing the information content of… (more)

Subjects/Keywords: data mining; text analysis; Perl

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APA (6^th Edition):

Hu, X. (2015). DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/25731

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hu, Xiaocheng. “DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH.” 2015. Thesis, Penn State University. Accessed February 27, 2021. https://submit-etda.libraries.psu.edu/catalog/25731.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hu, Xiaocheng. “DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH.” 2015. Web. 27 Feb 2021.

Vancouver:

Hu X. DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH. [Internet] [Thesis]. Penn State University; 2015. [cited 2021 Feb 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/25731.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hu X. DATA MINING ON CORPORATE FILLING BASED ON BAYESIAN LEARNING APPROACH. [Thesis]. Penn State University; 2015. Available from: https://submit-etda.libraries.psu.edu/catalog/25731

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universidade Nova

9. Viana, Hugo Henrique Amorim. Automatic information retrieval through text-mining.

Degree: 2013, Universidade Nova

URL: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11308

►

The dissertation presented for obtaining the Master’s Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Nowadays,… (more)

Subjects/Keywords: Text-mining; Stemmer; Clustering; Lucene

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APA (6^th Edition):

Viana, H. H. A. (2013). Automatic information retrieval through text-mining. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11308

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Viana, Hugo Henrique Amorim. “Automatic information retrieval through text-mining.” 2013. Thesis, Universidade Nova. Accessed February 27, 2021. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11308.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Viana, Hugo Henrique Amorim. “Automatic information retrieval through text-mining.” 2013. Web. 27 Feb 2021.

Vancouver:

Viana HHA. Automatic information retrieval through text-mining. [Internet] [Thesis]. Universidade Nova; 2013. [cited 2021 Feb 27]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11308.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Viana HHA. Automatic information retrieval through text-mining. [Thesis]. Universidade Nova; 2013. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11308

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Manchester

10. Gerner, Lars Martin Anders. Integrating text-mining approaches to identify entities and extract events from the biomedical literature.

Degree: PhD, 2012, University of Manchester

URL: https://www.research.manchester.ac.uk/portal/en/theses/integrating-textmining-approaches-to-identify-entities-and-extract-events-from-the-biomedical-literature(44f8e79a-3782-4687-85c7-eee1fda5cb76).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553476

► The amount of biomedical literature available is increasing at an exponential rate and is becoming increasingly difficult to navigate. Text-mining methods can potentially mitigate this… (more)

Subjects/Keywords: 006.312; Biomedical text mining

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APA (6^th Edition):

Gerner, L. M. A. (2012). Integrating text-mining approaches to identify entities and extract events from the biomedical literature. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/integrating-textmining-approaches-to-identify-entities-and-extract-events-from-the-biomedical-literature(44f8e79a-3782-4687-85c7-eee1fda5cb76).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553476

Chicago Manual of Style (16^th Edition):

Gerner, Lars Martin Anders. “Integrating text-mining approaches to identify entities and extract events from the biomedical literature.” 2012. Doctoral Dissertation, University of Manchester. Accessed February 27, 2021. https://www.research.manchester.ac.uk/portal/en/theses/integrating-textmining-approaches-to-identify-entities-and-extract-events-from-the-biomedical-literature(44f8e79a-3782-4687-85c7-eee1fda5cb76).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553476.

MLA Handbook (7^th Edition):

Gerner, Lars Martin Anders. “Integrating text-mining approaches to identify entities and extract events from the biomedical literature.” 2012. Web. 27 Feb 2021.

Vancouver:

Gerner LMA. Integrating text-mining approaches to identify entities and extract events from the biomedical literature. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2021 Feb 27]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/integrating-textmining-approaches-to-identify-entities-and-extract-events-from-the-biomedical-literature(44f8e79a-3782-4687-85c7-eee1fda5cb76).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553476.

Council of Science Editors:

Gerner LMA. Integrating text-mining approaches to identify entities and extract events from the biomedical literature. [Doctoral Dissertation]. University of Manchester; 2012. Available from: https://www.research.manchester.ac.uk/portal/en/theses/integrating-textmining-approaches-to-identify-entities-and-extract-events-from-the-biomedical-literature(44f8e79a-3782-4687-85c7-eee1fda5cb76).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553476

California State University – Sacramento

11. Balasubramanian, Sinduja. Text mining on Amazon reviews to extract feature based feedback.

Degree: MS, Computer Science, 2017, California State University – Sacramento

URL: http://hdl.handle.net/10211.3/198628

► Analyzing the feedback on a product or a service helps to improve the quality of the product or service. That way, reviews from online shopping… (more)

Subjects/Keywords: NLP; Text mining; Feature extraction

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APA (6^th Edition):

Balasubramanian, S. (2017). Text mining on Amazon reviews to extract feature based feedback. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/198628

Chicago Manual of Style (16^th Edition):

Balasubramanian, Sinduja. “Text mining on Amazon reviews to extract feature based feedback.” 2017. Masters Thesis, California State University – Sacramento. Accessed February 27, 2021. http://hdl.handle.net/10211.3/198628.

MLA Handbook (7^th Edition):

Balasubramanian, Sinduja. “Text mining on Amazon reviews to extract feature based feedback.” 2017. Web. 27 Feb 2021.

Vancouver:

Balasubramanian S. Text mining on Amazon reviews to extract feature based feedback. [Internet] [Masters thesis]. California State University – Sacramento; 2017. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10211.3/198628.

Council of Science Editors:

Balasubramanian S. Text mining on Amazon reviews to extract feature based feedback. [Masters Thesis]. California State University – Sacramento; 2017. Available from: http://hdl.handle.net/10211.3/198628

University of Manchester

12. Florez Vargas, Oscar Roberto. DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY.

Degree: 2016, University of Manchester

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:302324

► The idea that the same experimental findings can be reproduced by a variety of independent approaches is one of the cornerstones of science’s claim to… (more)

▼ The idea that the same experimental findings can be reproduced by a variety of independent approaches is one of the cornerstones of science’s claim to objective truth. However, in recent years, it has become clear that science is plagued by findings that cannot be reproduced and, consequently, invalidating research studies and undermining public trust in the research enterprise. The observed lack of reproducibility may be a result, among other things, of the lack of transparency or completeness in reporting. In particular, omissions in reporting the technical nature of the experimental method make it difficult to verify the findings of experimental research in biomedicine. In this context, the assessment of scientific reports could help to overcome – at least in part – the ongoing reproducibility crisis.In addressing this issue, this Thesis undertakes the challenge of developing strategies for the evaluation of reporting biomedical experimental methods in scientific manuscripts. Considering the complexity of experimental design – often involving different technologies and models, we characterise the problem in methods reporting through domain-specific checklists. Then, by using checklists as a decision making tool, supported by miniRECH – a spreadsheet-based approach that can be used by authors, editors and peer-reviewers – a reasonable level of consensus on reporting assessments was achieved regardless of the domain-specific expertise of referees. In addition, by using a text-mining system as a screening tool, a framework to guide an automated assessment of the reporting of bio-experiments was created. The usefulness of these strategies was demonstrated in some domain-specific scientific areas as well as in mouse models across biomedical research.In conclusion, we suggested that the strategies developed in this work could be implemented through the publication process as barriers to prevent incomplete reporting from entering the scientific literature, as well as promoters of completeness in reporting to improve the general value of the scientific evidence. Advisors/Committee Members: STEVENS, ROBERT RD, Brass, Andrew, Stevens, Robert.

Subjects/Keywords: Reproducibility; Checklists; Text Mining; Biomedicine

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APA (6^th Edition):

Florez Vargas, O. R. (2016). DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:302324

Chicago Manual of Style (16^th Edition):

Florez Vargas, Oscar Roberto. “DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY.” 2016. Doctoral Dissertation, University of Manchester. Accessed February 27, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:302324.

MLA Handbook (7^th Edition):

Florez Vargas, Oscar Roberto. “DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY.” 2016. Web. 27 Feb 2021.

Vancouver:

Florez Vargas OR. DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY. [Internet] [Doctoral dissertation]. University of Manchester; 2016. [cited 2021 Feb 27]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:302324.

Council of Science Editors:

Florez Vargas OR. DEVELOPMENT OF STRATEGIES FOR ASSESSING REPORTING IN BIOMEDICAL RESEARCH: MOVING TOWARD ENHANCING REPRODUCIBILITY. [Doctoral Dissertation]. University of Manchester; 2016. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:302324

University of Texas – Austin

13. Yang, Yongpeng. Mining of identity theft stories to model and assess identity threat behaviors.

Degree: MSin Engineering, Electrical and Computer Engineering, 2014, University of Texas – Austin

URL: http://hdl.handle.net/2152/26008

► Identity theft is an ever-present and ever-growing issue in our society. Identity theft, fraud and abuse are present and growing in every market sector. The… (more)

Subjects/Keywords: Identity theft; Text mining

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APA (6^th Edition):

Yang, Y. (2014). Mining of identity theft stories to model and assess identity threat behaviors. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/26008

Chicago Manual of Style (16^th Edition):

Yang, Yongpeng. “Mining of identity theft stories to model and assess identity threat behaviors.” 2014. Masters Thesis, University of Texas – Austin. Accessed February 27, 2021. http://hdl.handle.net/2152/26008.

MLA Handbook (7^th Edition):

Yang, Yongpeng. “Mining of identity theft stories to model and assess identity threat behaviors.” 2014. Web. 27 Feb 2021.

Vancouver:

Yang Y. Mining of identity theft stories to model and assess identity threat behaviors. [Internet] [Masters thesis]. University of Texas – Austin; 2014. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2152/26008.

Council of Science Editors:

Yang Y. Mining of identity theft stories to model and assess identity threat behaviors. [Masters Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/26008

Iowa State University

14. Zhai, Shuang. Business analytics using machine learning and large-scale textual data: Three essays.

Degree: 2020, Iowa State University

URL: https://lib.dr.iastate.edu/etd/18255

► Natural Language Processing (NLP) techniques have been recognized as useful tools in various applications, such as language translation, stock price prediction, and semantic analysis. In… (more)

▼ Natural Language Processing (NLP) techniques have been recognized as useful tools in various applications, such as language translation, stock price prediction, and semantic analysis. In recent years, the larger amount of data and faster computational hardware become available, NLP techniques are applied in a much broader application spectrum, and business applications are the frontier. My dissertation works at the intersection of business, Information Systems, and NLP. It extends NLP application scenarios in the business setting and employs NLP techniques in different real-world scenarios, including document topic categorization, company performance prediction, and corporate event sequence prediction. In particular, these problems are addressed by topic modeling techniques, recurrent neural networks, and sequence-to-sequence neural network architecture. My dissertation comprises three independent essays. The first essay utilizes topic modeling techniques to demonstrate the scientific topic trends and topic morphing for top Management Information System (MIS) publications. The second essay proposes a recurrent neural network model to predict multiple company financial ratios from publicly available news articles. The third essay first formulates a unique business question, predicting corporate event sequences, based on the historical event sequences submitted in the U.S. Securities and Exchange Commission (SEC) filings. Then, it uses the Transformer architecture to address the problem. In these essays, I apply and extend a variety of NLP tools to solve real-world business problems, including analyzing discipline publication trends and morphing, predicting company financial performance, and predicting corporate event sequences. NLP techniques employed in my dissertation include topic modeling, recurrent neural networks, and sequence-to-sequence networks. These essays contribute to discipline study and financial technology literature and develop tools for scholars, investors, and organizations to make informed decisions, from natural language.

Subjects/Keywords: Business Analytics; Text Mining

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhai, S. (2020). Business analytics using machine learning and large-scale textual data: Three essays. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/18255

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zhai, Shuang. “Business analytics using machine learning and large-scale textual data: Three essays.” 2020. Thesis, Iowa State University. Accessed February 27, 2021. https://lib.dr.iastate.edu/etd/18255.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zhai, Shuang. “Business analytics using machine learning and large-scale textual data: Three essays.” 2020. Web. 27 Feb 2021.

Vancouver:

Zhai S. Business analytics using machine learning and large-scale textual data: Three essays. [Internet] [Thesis]. Iowa State University; 2020. [cited 2021 Feb 27]. Available from: https://lib.dr.iastate.edu/etd/18255.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhai S. Business analytics using machine learning and large-scale textual data: Three essays. [Thesis]. Iowa State University; 2020. Available from: https://lib.dr.iastate.edu/etd/18255

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Aberdeen

15. Barawi, Mohamad Hardyman. Automatic topic labelling and opinion summarisation.

Degree: PhD, 2019, University of Aberdeen

URL: https://abdn.alma.exlibrisgroup.com/view/delivery/44ABE_INST/12153054170005941 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794105

► With the global increase in online tools such as online reviews and social media platforms, individuals all around the globe have changed their way of… (more)

Subjects/Keywords: 004; Text data mining; Probabilities

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APA (6^th Edition):

Barawi, M. H. (2019). Automatic topic labelling and opinion summarisation. (Doctoral Dissertation). University of Aberdeen. Retrieved from https://abdn.alma.exlibrisgroup.com/view/delivery/44ABE_INST/12153054170005941 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794105

Chicago Manual of Style (16^th Edition):

Barawi, Mohamad Hardyman. “Automatic topic labelling and opinion summarisation.” 2019. Doctoral Dissertation, University of Aberdeen. Accessed February 27, 2021. https://abdn.alma.exlibrisgroup.com/view/delivery/44ABE_INST/12153054170005941 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794105.

MLA Handbook (7^th Edition):

Barawi, Mohamad Hardyman. “Automatic topic labelling and opinion summarisation.” 2019. Web. 27 Feb 2021.

Vancouver:

Barawi MH. Automatic topic labelling and opinion summarisation. [Internet] [Doctoral dissertation]. University of Aberdeen; 2019. [cited 2021 Feb 27]. Available from: https://abdn.alma.exlibrisgroup.com/view/delivery/44ABE_INST/12153054170005941 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794105.

Council of Science Editors:

Barawi MH. Automatic topic labelling and opinion summarisation. [Doctoral Dissertation]. University of Aberdeen; 2019. Available from: https://abdn.alma.exlibrisgroup.com/view/delivery/44ABE_INST/12153054170005941 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794105

University of Sydney

16. Southavilay, Vilaythong. A Data Mining Toolbox for Collaborative Writing Processes .

Degree: 2013, University of Sydney

URL: http://hdl.handle.net/2123/9764

► Collaborative writing (CW) is an essential skill in academia and industry. Providing support during the process of CW can be useful not only for achieving… (more)

▼ Collaborative writing (CW) is an essential skill in academia and industry. Providing support during the process of CW can be useful not only for achieving better quality documents, but also for improving the CW skills of the writers. In order to properly support collaborative writing, it is essential to understand how ideas and concepts are developed during the writing process, which consists of a series of steps of writing activities. These steps can be considered as sequence patterns comprising both time events and the semantics of the changes made during those steps. Two techniques can be combined to examine those patterns: process mining, which focuses on extracting process-related knowledge from event logs recorded by an information system; and semantic analysis, which focuses on extracting knowledge about what the student wrote or edited. This thesis contributes (i) techniques to automatically extract process models of collaborative writing processes and (ii) visualisations to describe aspects of collaborative writing. These two techniques form a data mining toolbox for collaborative writing by using process mining, probabilistic graphical models, and text mining. First, I created a framework, WriteProc, for investigating collaborative writing processes, integrated with the existing cloud computing writing tools in Google Docs. Secondly, I created new heuristic to extract the semantic nature of text edits that occur in the document revisions and automatically identify the corresponding writing activities. Thirdly, based on sequences of writing activities, I propose methods to discover the writing process models and transitional state diagrams using a process mining algorithm, Heuristics Miner, and Hidden Markov Models, respectively. Finally, I designed three types of visualisations and made contributions to their underlying techniques for analysing writing processes. All components of the toolbox are validated against annotated writing activities of real documents and a synthetic dataset. I also illustrate how the automatically discovered process models and visualisations are used in the process analysis with real documents written by groups of graduate students. I discuss how the analyses can be used to gain further insight into how students work and create their collaborative documents.

Subjects/Keywords: process mining; text mining; collaborative writing

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APA (6^th Edition):

Southavilay, V. (2013). A Data Mining Toolbox for Collaborative Writing Processes . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/9764

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Southavilay, Vilaythong. “A Data Mining Toolbox for Collaborative Writing Processes .” 2013. Thesis, University of Sydney. Accessed February 27, 2021. http://hdl.handle.net/2123/9764.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Southavilay, Vilaythong. “A Data Mining Toolbox for Collaborative Writing Processes .” 2013. Web. 27 Feb 2021.

Vancouver:

Southavilay V. A Data Mining Toolbox for Collaborative Writing Processes . [Internet] [Thesis]. University of Sydney; 2013. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2123/9764.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Southavilay V. A Data Mining Toolbox for Collaborative Writing Processes . [Thesis]. University of Sydney; 2013. Available from: http://hdl.handle.net/2123/9764

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Cambridge

17. Jessop, David M. Information extraction from chemical patents.

Degree: PhD, 2011, University of Cambridge

URL: http://www.dspace.cam.ac.uk/handle/1810/238302https://www.repository.cam.ac.uk/bitstream/1810/238302/3/supportingInformation.zip ; https://www.repository.cam.ac.uk/bitstream/1810/238302/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/238302/7/thesis-final.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/238302/8/thesis-final.pdf.jpg

► The automated extraction of semantic chemical data from the existing literature is demonstrated. For reasons of copyright, the work is focused on the patent literature,… (more)

Subjects/Keywords: information extraction; text mining; chemical patents; patents; chemical text mining; PatentEye

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APA (6^th Edition):

Jessop, D. M. (2011). Information extraction from chemical patents. (Doctoral Dissertation). University of Cambridge. Retrieved from http://www.dspace.cam.ac.uk/handle/1810/238302https://www.repository.cam.ac.uk/bitstream/1810/238302/3/supportingInformation.zip ; https://www.repository.cam.ac.uk/bitstream/1810/238302/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/238302/7/thesis-final.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/238302/8/thesis-final.pdf.jpg

Chicago Manual of Style (16^th Edition):

Jessop, David M. “Information extraction from chemical patents.” 2011. Doctoral Dissertation, University of Cambridge. Accessed February 27, 2021. http://www.dspace.cam.ac.uk/handle/1810/238302https://www.repository.cam.ac.uk/bitstream/1810/238302/3/supportingInformation.zip ; https://www.repository.cam.ac.uk/bitstream/1810/238302/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/238302/7/thesis-final.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/238302/8/thesis-final.pdf.jpg.

MLA Handbook (7^th Edition):

Jessop, David M. “Information extraction from chemical patents.” 2011. Web. 27 Feb 2021.

Vancouver:

Jessop DM. Information extraction from chemical patents. [Internet] [Doctoral dissertation]. University of Cambridge; 2011. [cited 2021 Feb 27]. Available from: http://www.dspace.cam.ac.uk/handle/1810/238302https://www.repository.cam.ac.uk/bitstream/1810/238302/3/supportingInformation.zip ; https://www.repository.cam.ac.uk/bitstream/1810/238302/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/238302/7/thesis-final.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/238302/8/thesis-final.pdf.jpg.

Council of Science Editors:

Jessop DM. Information extraction from chemical patents. [Doctoral Dissertation]. University of Cambridge; 2011. Available from: http://www.dspace.cam.ac.uk/handle/1810/238302https://www.repository.cam.ac.uk/bitstream/1810/238302/3/supportingInformation.zip ; https://www.repository.cam.ac.uk/bitstream/1810/238302/4/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/238302/7/thesis-final.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/238302/8/thesis-final.pdf.jpg

Linköping University

18. Benis, Nirupama. Corpus construction based on Ontological domain knowledge.

Degree: Computer and Information Science, 2011, Linköping University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71851

► The purpose of this thesis is to contribute a corpus for sentence level interpretation of biomedical language. The available corpora for the biomedical domain… (more)

Subjects/Keywords: Text mining; Biomedical text mining; Natural Language Processing

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APA (6^th Edition):

Benis, N. (2011). Corpus construction based on Ontological domain knowledge. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71851

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Benis, Nirupama. “Corpus construction based on Ontological domain knowledge.” 2011. Thesis, Linköping University. Accessed February 27, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71851.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Benis, Nirupama. “Corpus construction based on Ontological domain knowledge.” 2011. Web. 27 Feb 2021.

Vancouver:

Benis N. Corpus construction based on Ontological domain knowledge. [Internet] [Thesis]. Linköping University; 2011. [cited 2021 Feb 27]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71851.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Benis N. Corpus construction based on Ontological domain knowledge. [Thesis]. Linköping University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71851

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Cambridge

19. Lowe, Daniel Mark. Extraction of chemical structures and reactions from the literature.

Degree: PhD, 2012, University of Cambridge

URL: http://www.dspace.cam.ac.uk/handle/1810/244727https://www.repository.cam.ac.uk/bitstream/1810/244727/2/Extraction%20of%20chemical%20structures%20and%20reactions%20from%20the%20literature_Supporting%20Information.zip ; https://www.repository.cam.ac.uk/bitstream/1810/244727/3/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244727/4/lowethesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244727/5/lowethesis.pdf.jpg

► The ever increasing quantity of chemical literature necessitates the creation of automated techniques for extracting relevant information. This work focuses on two aspects: the conversion… (more)

▼ The ever increasing quantity of chemical literature necessitates the creation of automated techniques for extracting relevant information. This work focuses on two aspects: the conversion of chemical names to computer readable structure representations and the extraction of chemical reactions from text. Chemical names are a common way of communicating chemical structure information. OPSIN (Open Parser for Systematic IUPAC Nomenclature), an open source, freely available algorithm for converting chemical names to structures was developed. OPSIN employs a regular grammar to direct tokenisation and parsing leading to the generation of an XML parse tree. Nomenclature operations are applied successively to the tree with many requiring the manipulation of an in-memory connection table representation of the structure under construction. Areas of nomenclature supported are described with attention being drawn to difficulties that may be encountered in name to structure conversion. Results on sets of generated names and names extracted from patents are presented. On generated names, recall of between 96.2% and 99.0% was achieved with a lower bound of 97.9% on precision with all results either being comparable or superior to the tested commercial solutions. On the patent names OPSIN s recall was 2-10% higher than the tested solutions when the patent names were processed as found in the patents. The uses of OPSIN as a web service and as a tool for identifying chemical names in text are shown to demonstrate the direct utility of this algorithm. A software system for extracting chemical reactions from the text of chemical patents was developed. The system relies on the output of ChemicalTagger, a tool for tagging words and identifying phrases of importance in experimental chemistry text. Improvements to this tool required to facilitate this task are documented. The structure of chemical entities are where possible determined using OPSIN in conjunction with a dictionary of name to structure relationships. Extracted reactions are atom mapped to confirm that they are chemically consistent. 424,621 atom mapped reactions were extracted from 65,034 organic chemistry USPTO patents. On a sample of 100 of these extracted reactions chemical entities were identified with 96.4% recall and 88.9% precision. Quantities could be associated with reagents in 98.8% of cases and 64.9% of cases for products whilst the correct role was assigned to chemical entities in 91.8% of cases. Qualitatively the system captured the essence of the reaction in 95% of cases. This system is expected to be useful in the creation of searchable databases of reactions from chemical patents and in facilitating analysis of the properties of large populations of reactions.

Subjects/Keywords: Name to structure; OPSIN; Chemical text mining; Text mining; Patent reaction extraction; Reaction mining; Patents

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APA (6^th Edition):

Lowe, D. M. (2012). Extraction of chemical structures and reactions from the literature. (Doctoral Dissertation). University of Cambridge. Retrieved from http://www.dspace.cam.ac.uk/handle/1810/244727https://www.repository.cam.ac.uk/bitstream/1810/244727/2/Extraction%20of%20chemical%20structures%20and%20reactions%20from%20the%20literature_Supporting%20Information.zip ; https://www.repository.cam.ac.uk/bitstream/1810/244727/3/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244727/4/lowethesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244727/5/lowethesis.pdf.jpg

Chicago Manual of Style (16^th Edition):

Lowe, Daniel Mark. “Extraction of chemical structures and reactions from the literature.” 2012. Doctoral Dissertation, University of Cambridge. Accessed February 27, 2021. http://www.dspace.cam.ac.uk/handle/1810/244727https://www.repository.cam.ac.uk/bitstream/1810/244727/2/Extraction%20of%20chemical%20structures%20and%20reactions%20from%20the%20literature_Supporting%20Information.zip ; https://www.repository.cam.ac.uk/bitstream/1810/244727/3/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244727/4/lowethesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244727/5/lowethesis.pdf.jpg.

MLA Handbook (7^th Edition):

Lowe, Daniel Mark. “Extraction of chemical structures and reactions from the literature.” 2012. Web. 27 Feb 2021.

Vancouver:

Lowe DM. Extraction of chemical structures and reactions from the literature. [Internet] [Doctoral dissertation]. University of Cambridge; 2012. [cited 2021 Feb 27]. Available from: http://www.dspace.cam.ac.uk/handle/1810/244727https://www.repository.cam.ac.uk/bitstream/1810/244727/2/Extraction%20of%20chemical%20structures%20and%20reactions%20from%20the%20literature_Supporting%20Information.zip ; https://www.repository.cam.ac.uk/bitstream/1810/244727/3/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244727/4/lowethesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244727/5/lowethesis.pdf.jpg.

Council of Science Editors:

Lowe DM. Extraction of chemical structures and reactions from the literature. [Doctoral Dissertation]. University of Cambridge; 2012. Available from: http://www.dspace.cam.ac.uk/handle/1810/244727https://www.repository.cam.ac.uk/bitstream/1810/244727/2/Extraction%20of%20chemical%20structures%20and%20reactions%20from%20the%20literature_Supporting%20Information.zip ; https://www.repository.cam.ac.uk/bitstream/1810/244727/3/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244727/4/lowethesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244727/5/lowethesis.pdf.jpg

Queensland University of Technology

20. Albathan, Mubarak Murdi M. Enhancement of relevant features for text mining.

Degree: 2015, Queensland University of Technology

URL: https://eprints.qut.edu.au/90072/

► With the explosion of information resources, there is an imminent need to understand interesting text features or topics in massive text information. This thesis proposes… (more)

Subjects/Keywords: Text Mining; Feature Selection; Information retrieval; Data Mining; pattern mining

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APA (6^th Edition):

Albathan, M. M. M. (2015). Enhancement of relevant features for text mining. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/90072/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Albathan, Mubarak Murdi M. “Enhancement of relevant features for text mining.” 2015. Thesis, Queensland University of Technology. Accessed February 27, 2021. https://eprints.qut.edu.au/90072/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Albathan, Mubarak Murdi M. “Enhancement of relevant features for text mining.” 2015. Web. 27 Feb 2021.

Vancouver:

Albathan MMM. Enhancement of relevant features for text mining. [Internet] [Thesis]. Queensland University of Technology; 2015. [cited 2021 Feb 27]. Available from: https://eprints.qut.edu.au/90072/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Albathan MMM. Enhancement of relevant features for text mining. [Thesis]. Queensland University of Technology; 2015. Available from: https://eprints.qut.edu.au/90072/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Open Universiteit Nederland

21. van Rouwendal, J. (Jan). Tekst en association rule mining voor detecteren van workarounds in vrije tekst die gestructureerde data-invoer omzeilen.

Degree: Master, Department of Information Science, 2020, Open Universiteit Nederland

URL: http://hdl.handle.net/1820/81ed7548-0b4c-4a95-8ad2-5aa6a0c97375

► Veel informatiesystemen hebben functionele beperkingen om structurele data in te voeren en gebruikers kunnen dit omzeilen door vrije tekst in het informatiesysteem te gebruiken als… (more)

Subjects/Keywords: shadow-IT; workarounds; tekstmining; text mining; association rule mining; resilience mining

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APA (6^th Edition):

van Rouwendal, J. (. (2020). Tekst en association rule mining voor detecteren van workarounds in vrije tekst die gestructureerde data-invoer omzeilen. (Masters Thesis). Open Universiteit Nederland. Retrieved from http://hdl.handle.net/1820/81ed7548-0b4c-4a95-8ad2-5aa6a0c97375

Chicago Manual of Style (16^th Edition):

van Rouwendal, J (Jan). “Tekst en association rule mining voor detecteren van workarounds in vrije tekst die gestructureerde data-invoer omzeilen.” 2020. Masters Thesis, Open Universiteit Nederland. Accessed February 27, 2021. http://hdl.handle.net/1820/81ed7548-0b4c-4a95-8ad2-5aa6a0c97375.

MLA Handbook (7^th Edition):

van Rouwendal, J (Jan). “Tekst en association rule mining voor detecteren van workarounds in vrije tekst die gestructureerde data-invoer omzeilen.” 2020. Web. 27 Feb 2021.

Vancouver:

van Rouwendal J(. Tekst en association rule mining voor detecteren van workarounds in vrije tekst die gestructureerde data-invoer omzeilen. [Internet] [Masters thesis]. Open Universiteit Nederland; 2020. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1820/81ed7548-0b4c-4a95-8ad2-5aa6a0c97375.

Council of Science Editors:

van Rouwendal J(. Tekst en association rule mining voor detecteren van workarounds in vrije tekst die gestructureerde data-invoer omzeilen. [Masters Thesis]. Open Universiteit Nederland; 2020. Available from: http://hdl.handle.net/1820/81ed7548-0b4c-4a95-8ad2-5aa6a0c97375

University of Windsor

22. Ejieh, Chukwuma. ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS.

Degree: MS, Computer Science, 2016, University of Windsor

URL: https://scholar.uwindsor.ca/etd/5728

► Aspect-based Opinion Mining (ABOM) systems take as input a corpus about a product and aim to mine the aspects (the features or parts) of the… (more)

Subjects/Keywords: Aspect-based Opinion Mining; Data Mining; Machine Learning; Microblogs; Opinion Mining; Text Mining

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APA (6^th Edition):

Ejieh, C. (2016). ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/5728

Chicago Manual of Style (16^th Edition):

Ejieh, Chukwuma. “ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS.” 2016. Masters Thesis, University of Windsor. Accessed February 27, 2021. https://scholar.uwindsor.ca/etd/5728.

MLA Handbook (7^th Edition):

Ejieh, Chukwuma. “ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS.” 2016. Web. 27 Feb 2021.

Vancouver:

Ejieh C. ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS. [Internet] [Masters thesis]. University of Windsor; 2016. [cited 2021 Feb 27]. Available from: https://scholar.uwindsor.ca/etd/5728.

Council of Science Editors:

Ejieh C. ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS. [Masters Thesis]. University of Windsor; 2016. Available from: https://scholar.uwindsor.ca/etd/5728

University of Vienna

23. Wilfinger, Lukas. Möglichkeiten zur Lokalisation von Standorten anhand von quantitativen Entfernungsangaben in Texten mittels Textmining am Beispiel Airbnb.

Degree: 2019, University of Vienna

URL: http://othes.univie.ac.at/59559/

►

Innerhalb von textuellen Daten gibt es ein Potenzial zur Gewinnung räumlicher Information. Dies kann helfen Personen aufgrund deren relativen Entfernungsangaben zu lokalisieren, ohne dassdabei eine… (more)

▼

Innerhalb von textuellen Daten gibt es ein Potenzial zur Gewinnung räumlicher Information. Dies kann helfen Personen aufgrund deren relativen Entfernungsangaben zu lokalisieren, ohne dassdabei eine umfangreiche Kenntnis der Umgebung vorausgesetzt wird. Um diesen heuristischen Ansatz zu testen, wurden als Basis die Daten von der Onlineplattform Airbnb verwendet. Die Tauglichkeit dieser Quelle liegt darin begründet, da die relative Lage der Unterkunft zu touristischen Sehenswürdigkeiten ein wichtiges Kriterium bei der Buchung ist. Zur Ermittlung von relevanten Tripletten aus dem vorhandenen Korpus wurden Techniken aus dem Textmining und dem Geoparsing eingesetzt.Dies sorgte dafür, dass nur Geo-text Daten für die Analyse übrigblieben, also nur Informationen,die ein Potenzial zur Findung von Positionen aufgrund von quantitativen Entfernungsangabenaufwiesen. Es hat sich gezeigt, dass von den 293 detektierten Sätzen,20% als relevante Distanzinformationenin ein Geoinformationssystem (GIS)überführt werden konnten.Es wurde auf eine händische Kodierung zurückgegriffen, dain den Sätzen semantische Mehrdeutigkeiten auftraten und das Relatumdurch den Computeroftmalsnicht zu identifizierenwar. Die darauffolgende Analyse hat gezeigt, dass bei zwei relativen Entfernungsangaben keine weitere Bearbeitung notwendig ist und dadurch der Abstand zum Potenzialbereichin dem sich das Airbnb befindet,auf 13,8m verringert wurde. Bei Vorhandensein von mehr als zwei Angaben musste eine visuelle Interpretation angewandtwerden, da die Unsicherheiten zu groß waren. Es entstehen mehrere Überschneidungsbereiche, die teilweise nicht automatisch zu reduzieren sind. Nichtsdestotrotz kann sogar in diesen Fällen, bei zusätzlicher Berücksichtigung der Realnutzung der Gebäude, der relevante Häuserblock bestimmt werden.

Within textual data there is a potential for obtaining spatial information. This can help to locate people based on their relative distance information, without requiring extensive knowledge of the environment. To test this heuristic approach, the data from the Airbnb online platform was used as the basis.The suitability of this source is sincethe relative location of the accommodation to tourist attractions is an important criterion when booking. Text mining and geoparsing techniques were used to determine relevant triplets from the existing corpus. This ensured that only geo-text data remained for the analysis, that is, theoretically only information that had the potential to find positions based on quantitative distance information. It has been shown that of the 293 detected sentences, 20% could be transferred as relevant distance information into a geographic information system (GIS). It was based on a manual coding, as in the sentences semantic ambiguities occurred and the relatum was often not identified by the computer. The subsequent analysis showed that with two relative distance indications, no further processing is necessary, thereby reducing the distance to the potential area in which the Airbnb is located to 13.8m. In the…

Subjects/Keywords: 54.82 Textverarbeitung; 74.48 Geoinformationssysteme; Data Mining / GIS / Text Mining / Airbnb / Geoparsing; Data Mining / GIS / Text Mining / Airbnb / Geoparsing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wilfinger, L. (2019). Möglichkeiten zur Lokalisation von Standorten anhand von quantitativen Entfernungsangaben in Texten mittels Textmining am Beispiel Airbnb. (Thesis). University of Vienna. Retrieved from http://othes.univie.ac.at/59559/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wilfinger, Lukas. “Möglichkeiten zur Lokalisation von Standorten anhand von quantitativen Entfernungsangaben in Texten mittels Textmining am Beispiel Airbnb.” 2019. Thesis, University of Vienna. Accessed February 27, 2021. http://othes.univie.ac.at/59559/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wilfinger, Lukas. “Möglichkeiten zur Lokalisation von Standorten anhand von quantitativen Entfernungsangaben in Texten mittels Textmining am Beispiel Airbnb.” 2019. Web. 27 Feb 2021.

Vancouver:

Wilfinger L. Möglichkeiten zur Lokalisation von Standorten anhand von quantitativen Entfernungsangaben in Texten mittels Textmining am Beispiel Airbnb. [Internet] [Thesis]. University of Vienna; 2019. [cited 2021 Feb 27]. Available from: http://othes.univie.ac.at/59559/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wilfinger L. Möglichkeiten zur Lokalisation von Standorten anhand von quantitativen Entfernungsangaben in Texten mittels Textmining am Beispiel Airbnb. [Thesis]. University of Vienna; 2019. Available from: http://othes.univie.ac.at/59559/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universidade de Lisboa

24. Martins, Bruno. Geographically Aware Web Text Mining.

Degree: 2009, Universidade de Lisboa

URL: http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/14301

► Text mining and search have become important research areas over the past few years, mostly due to the large popularity of the Web. A natural… (more)

Subjects/Keywords: Geographic Information Retrieval; Web/Text Mining

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Martins, B. (2009). Geographically Aware Web Text Mining. (Thesis). Universidade de Lisboa. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/14301

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Martins, Bruno. “Geographically Aware Web Text Mining.” 2009. Thesis, Universidade de Lisboa. Accessed February 27, 2021. http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/14301.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Martins, Bruno. “Geographically Aware Web Text Mining.” 2009. Web. 27 Feb 2021.

Vancouver:

Martins B. Geographically Aware Web Text Mining. [Internet] [Thesis]. Universidade de Lisboa; 2009. [cited 2021 Feb 27]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/14301.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Martins B. Geographically Aware Web Text Mining. [Thesis]. Universidade de Lisboa; 2009. Available from: http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/14301

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

25. Li, Jih-Pin. Explore Internet Users in Taiwan Neighbors Emotion.

Degree: Master, Information Management, 2014, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0614114-062227

► Traditional political science research usually adopts random sampling to choose a number of subjects and inquires their opinions towards certain issues. The result is then… (more)

Subjects/Keywords: LDA; political science; sentiment analysis; text mining

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APA (6^th Edition):

Li, J. (2014). Explore Internet Users in Taiwan Neighbors Emotion. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0614114-062227

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Jih-Pin. “Explore Internet Users in Taiwan Neighbors Emotion.” 2014. Thesis, NSYSU. Accessed February 27, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0614114-062227.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Jih-Pin. “Explore Internet Users in Taiwan Neighbors Emotion.” 2014. Web. 27 Feb 2021.

Vancouver:

Li J. Explore Internet Users in Taiwan Neighbors Emotion. [Internet] [Thesis]. NSYSU; 2014. [cited 2021 Feb 27]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0614114-062227.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li J. Explore Internet Users in Taiwan Neighbors Emotion. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0614114-062227

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Dalhousie University

26. Hu, Yeming. Document Clustering with Dual Supervision.

Degree: PhD, Faculty of Computer Science, 2012, Dalhousie University

URL: http://hdl.handle.net/10222/15056

► Nowadays, academic researchers maintain a personal library of papers, which they would like to organize based on their needs, e.g., research, projects, or courseware. Clustering… (more)

▼ Nowadays, academic researchers maintain a personal library of papers, which they would like to organize based on their needs, e.g., research, projects, or courseware. Clustering techniques are often employed to achieve this goal by grouping the document collection into different topics. Unsupervised clustering does not require any user effort but only produces one universal output with which users may not be satisfied. Therefore, document clustering needs user input for guidance to generate personalized clusters for different users. Semi-supervised clustering incorporates prior information and has the potential to produce customized clusters. Traditional semi-supervised clustering is based on user supervision in the form of labeled instances or pairwise instance constraints. However, alternative forms of user supervision exist such as labeling features. For document clustering, document supervision involves labeling documents while feature supervision involves labeling features. Their joint of use has been called dual supervision. In this thesis, we first explore and propose a framework to use feature supervision for interactive feature selection by indicating whether a feature is useful for clustering. Second, we enhance the semi-supervised clustering with feature supervision using feature reweighting. Third, we propose a unified framework to combine document supervision and feature supervision through seeding. The newly proposed algorithms are evaluated using oracles and demonstrated to be more helpful in producing better clusters matching a single user's point of view than document clustering without any supervision and with only document supervision. Finally, we conduct a user study to confirm that different users have different understandings of the same document collection and prefer personalized clusters. At the same time, we demonstrate that document clustering with dual supervision is able to produce good personalized clusters even with noisy user input. Dual supervision is also demonstrated to be more effective in personalized clustering than no supervision or any single supervision. We also analyze users' behaviors during the user study and present suggestions for the design of document management software. Advisors/Committee Members: Dr. Mario A. Nascimento (external-examiner), Dr. Qigang Gao (graduate-coordinator), Dr. Vlado Keselj (thesis-reader), Dr. Qigang Gao (thesis-reader), Dr. Evangelos E. Milios and Dr. James Blustein (thesis-supervisor), Received (ethics-approval), Yes (manuscripts), Yes (copyright-release).

Subjects/Keywords: Document Management; Text Mining; Machine Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hu, Y. (2012). Document Clustering with Dual Supervision. (Doctoral Dissertation). Dalhousie University. Retrieved from http://hdl.handle.net/10222/15056

Chicago Manual of Style (16^th Edition):

Hu, Yeming. “Document Clustering with Dual Supervision.” 2012. Doctoral Dissertation, Dalhousie University. Accessed February 27, 2021. http://hdl.handle.net/10222/15056.

MLA Handbook (7^th Edition):

Hu, Yeming. “Document Clustering with Dual Supervision.” 2012. Web. 27 Feb 2021.

Vancouver:

Hu Y. Document Clustering with Dual Supervision. [Internet] [Doctoral dissertation]. Dalhousie University; 2012. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10222/15056.

Council of Science Editors:

Hu Y. Document Clustering with Dual Supervision. [Doctoral Dissertation]. Dalhousie University; 2012. Available from: http://hdl.handle.net/10222/15056

University of Edinburgh

27. Edwards, Stephen. MilkMine: text-mining, milk proteins and hypothesis generation.

Degree: 2009, University of Edinburgh

URL: http://hdl.handle.net/1842/3869

► The vast and increasing volume of biological data can make it a struggle for scientists to keep up-to-date with the latest research and as a… (more)

▼ The vast and increasing volume of biological data can make it a struggle for scientists to keep up-to-date with the latest research and as a consequence they may miss significant biological links, particularly those that extend outwith their own area of expertise. MilkMine is an attempt to provide a single informatics resource to help milk protein scientists mine this information mountain more effectively, by integrating standard experimental data types with data generated by emerging text-mining techniques. A method was initially developed to identify milk-related terminology from peer-reviewed biological literature and this was used to complement the Unified Medical Language System (UMLS), a large thesaurus of biological concepts, their variant names and their types. The resultant enriched ontology was then mapped to the free text of peer-reviewed biological literature using the MMTx program producing a database of semantically enriched sentences. A co-occurrence relation extraction algorithm was written to identify relationships between milk proteins and peptides, and other biological concepts, such as diseases or biological processes. Using these literature relation sets new hypotheses can be generated using the basic principle that if “A is linked to B”, and if “B is linked to C” then we can infer an association between A and C. Filtering and downstream processing of the many generated relationships promotes significant interactions. These literature relations and hypotheses are integrated with biological data into the MilkMine database. The MilkMine database is built upon on a generic data warehousing system, InterMine. This tool enabled the integration of traditional data types, such as protein sequence or structural data, from a variety of sources (e.g. UniProt). However, the standard InterMine model was also extended by the author to include other data sources (e.g. the Protein Data Bank) and to incorporate the output of the text-mining algorithm. This integration of otherwise disparate information allows more complex querying of the data, across many data types. For example, protein sequences are mapped to instances of the names, synonyms or symbols of the protein in text, therefore a raw fragment of amino acid sequence (e.g. a particular binding region) can be used to search the MilkMine database for literature information as well as the interactions and hypotheses of those proteins that contain the sequence. The MilkMine resource is accessible online (www.bioinformatics.ed.ac.uk/milkmine) through a professional level query interface offering many features such as an interactive query builder, standard ready-to-run queries, bulk downloads and the ability to store user preferences and query histories. Evaluation of MilkMine showed that the text-mining algorithm, as well as the data integration, could provide the user with interesting connections for further study. Advisors/Committee Members: Sawyer, Lindsay, Webber, Bonnie.

Subjects/Keywords: text-mining; informatics; milk protein; milk peptide

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Edwards, S. (2009). MilkMine: text-mining, milk proteins and hypothesis generation. (Thesis). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/3869

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Edwards, Stephen. “MilkMine: text-mining, milk proteins and hypothesis generation.” 2009. Thesis, University of Edinburgh. Accessed February 27, 2021. http://hdl.handle.net/1842/3869.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Edwards, Stephen. “MilkMine: text-mining, milk proteins and hypothesis generation.” 2009. Web. 27 Feb 2021.

Vancouver:

Edwards S. MilkMine: text-mining, milk proteins and hypothesis generation. [Internet] [Thesis]. University of Edinburgh; 2009. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1842/3869.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Edwards S. MilkMine: text-mining, milk proteins and hypothesis generation. [Thesis]. University of Edinburgh; 2009. Available from: http://hdl.handle.net/1842/3869

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Manchester

28. Karystianis, George. Extraction and representation of key characteristics from epidemiological literature.

Degree: 2014, University of Manchester

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:220558

► Epidemiological studies are rich in information that could improve the understanding of concept complexity of a health problem, and are important sources for evidence based… (more)

▼ Epidemiological studies are rich in information that could improve the understanding of concept complexity of a health problem, and are important sources for evidence based medicine. However, epidemiologists experience difficulties in recognising and aggregating key characteristics in related research due to an increasing number of published articles. The main aim of this dissertation is to explore how text mining techniques can assist epidemiologists to identify important pieces of information and detect and integrate key knowledge for further research and exploration via concept maps. Concept maps are widely used in medicine for exploration and representation as a relatively formal, easy to design and understand knowledge representation model.To support this aim, we have developed a methodology for the extraction of key epidemiological characteristics from all types of epidemiological research articles in order to visualise, explore and aggregate concepts related to a health care problem. A generic rule-based approach was designed and implemented for the identification of mentions of six key characteristics, including study design, population, exposure, outcome, covariate and effect size. The system also relies on automatic term recognition and biomedical dictionaries to identify concepts of interests. In order to facilitate knowledge integration and aggregation, extracted characteristics are further normalized and mapped to existing resources. Study design mentions are mapped to an expanded version of the Ontology of Clinical Research (OCRe), whereas exposure, outcome and covariate mentions are mapped to Unified Medical Language System (UMLS) semantic groups and categories. Population mentions are mapped to age groups, gender and nationality/ethnicity, and effect size mentions are normalised with the regards to the used metric and confidence interval and related concept. The evaluation has shown reliable results, with an average micro F-score of 87% for recognition of epidemiological mentions and 91% for normalisation. Normalised concepts are further organised in an automatically generated concept map, which has three sections for exposures, outcomes and covariates.To demonstrate the potential of the developed methodology, it was applied to a large-scale corpus of epidemiological research abstracts related to obesity. Obesity was chosen as a case study since it has emerged as one of the most important global health problems of the 21st century. Using the concepts extracted from the corpus, we have built a searchable database of key epidemiological characteristics explored in obesity and an automatically generated concept map represented the normalized exposures, outcomes and covariates. An epidemiological workbench (EpiTeM) was designed to enable further exploration and inspection of the normalized extracted data, with direct links to the literature. The generated results also allow exploration of trends in obesity research and can facilitate understanding of its concept complexity. For example, we have noted the… Advisors/Committee Members: BUCHAN, IAIN IE, Nenadic, Goran, Buchan, Iain.

Subjects/Keywords: text mining; epidemiology; concept map; key characteristics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Karystianis, G. (2014). Extraction and representation of key characteristics from epidemiological literature. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:220558

Chicago Manual of Style (16^th Edition):

Karystianis, George. “Extraction and representation of key characteristics from epidemiological literature.” 2014. Doctoral Dissertation, University of Manchester. Accessed February 27, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:220558.

MLA Handbook (7^th Edition):

Karystianis, George. “Extraction and representation of key characteristics from epidemiological literature.” 2014. Web. 27 Feb 2021.

Vancouver:

Karystianis G. Extraction and representation of key characteristics from epidemiological literature. [Internet] [Doctoral dissertation]. University of Manchester; 2014. [cited 2021 Feb 27]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:220558.

Council of Science Editors:

Karystianis G. Extraction and representation of key characteristics from epidemiological literature. [Doctoral Dissertation]. University of Manchester; 2014. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:220558

Penn State University

29. Chen, Bi. Topic Oriented Evolution and Sentiment Analysis.

Degree: 2011, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/11743

► Topic modeling techniques help people to understand what is talking about in a corpus, and dramatically improve human’s work on academic or business productivity. Although… (more)

▼ Topic modeling techniques help people to understand what is talking about in a corpus, and dramatically improve human’s work on academic or business productivity. Although these topic modeling techniques can usually handle topic information represented by word frequencies well, they cannot deal with other unstructured text information such as timestamps, sentiments, or opinions. However, real applications often have needs to explain topics using both structured and unstructured text information together. When texts have their timestamps, it is necessary to identify different topics at different timestamps and how they evolve overtime. When sentiments or opinions are included in texts, it is necessary to identify human's opinion on certain aspects. Toward these challenges, novel models are explored in this thesis to solve problems of topic evolution and aspect-level sentiment analysis. Two novel models towards topic evolution as follows: (1) The first model, through exploiting social networks in blogsphere, can accurately predict what topics bloggers will talk about in future; and (2) The second model, by applying citation networks in scientific literature, can identify new research topics and how research topics evolve overtime. Three novel models towards aspect-level sentiment analysis as follows: (3) The first model, by incorporating sentiment lexicons as prior knowledge with machine learning approaches such as Support Vector Machine, can significantly improve the accuracy of sentiment analysis; (4) The second model, through semi-supervised Chinese Restaurant Process, can identify new aspects as well as their sentiments; and (5) The third model, through discovering associations between topic and opinion words, can identify opinionists' standpoints on certain topics. All Five models are rigorously validated using both real and synthetic experimental data. Experiments on these first proposed models are compared with our baselines, and the third model is compared with the state-of-the-art methods. The first model can predict future topics in blogsphere for next 4 weeks with high precision (0.94). The second model can construct the map of research topic evolution and measure topic influence with accuracy (0.65) comparable to human ratings (0.76). The third model can significantly improve the accuracy of sentiment analysis by 5% compared with the state of arts methods; The fourth model can find new aspects with high precision (0.82) and recall (0.78); The fifth model can find and visualize most controversial topics and extract opinion sentences to represent opinionists’ standpoints with high accuracy (0.97). Advisors/Committee Members: Dongwon Lee, Dissertation Advisor/Co-Advisor, Dongwon Lee, Committee Chair/Co-Chair, Xiaolong Zhang, Committee Member, Prasenjit Mitra, Committee Member, Daniel Kifer, Committee Member, Ji Fang, Committee Member.

Subjects/Keywords: text mining; topic modeling; sentiment analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, B. (2011). Topic Oriented Evolution and Sentiment Analysis. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/11743

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Bi. “Topic Oriented Evolution and Sentiment Analysis.” 2011. Thesis, Penn State University. Accessed February 27, 2021. https://submit-etda.libraries.psu.edu/catalog/11743.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Bi. “Topic Oriented Evolution and Sentiment Analysis.” 2011. Web. 27 Feb 2021.

Vancouver:

Chen B. Topic Oriented Evolution and Sentiment Analysis. [Internet] [Thesis]. Penn State University; 2011. [cited 2021 Feb 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/11743.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen B. Topic Oriented Evolution and Sentiment Analysis. [Thesis]. Penn State University; 2011. Available from: https://submit-etda.libraries.psu.edu/catalog/11743

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

30. Chang, Cheng-Jui. Cuisine Discovery based on Recipe-Ingredient Network and Matrix Factorization.

Degree: Master, Information Management, 2017, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0812117-134940

► This research proposes an approach to find the cuisines, the types of dishes, from the recipes, ingredients and methods of producing dishes. We believe that… (more)

Subjects/Keywords: clustering; network; recommendation; matrix factorization; text mining

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APA (6^th Edition):

Chang, C. (2017). Cuisine Discovery based on Recipe-Ingredient Network and Matrix Factorization. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0812117-134940

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chang, Cheng-Jui. “Cuisine Discovery based on Recipe-Ingredient Network and Matrix Factorization.” 2017. Thesis, NSYSU. Accessed February 27, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0812117-134940.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chang, Cheng-Jui. “Cuisine Discovery based on Recipe-Ingredient Network and Matrix Factorization.” 2017. Web. 27 Feb 2021.

Vancouver:

Chang C. Cuisine Discovery based on Recipe-Ingredient Network and Matrix Factorization. [Internet] [Thesis]. NSYSU; 2017. [cited 2021 Feb 27]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0812117-134940.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chang C. Cuisine Discovery based on Recipe-Ingredient Network and Matrix Factorization. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0812117-134940

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Open Access Theses and Dissertations