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You searched for +publisher:"University of Texas – Austin" +contributor:("Mooney, Raymond"). Showing records 1 – 18 of 18 total matches.

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University of Texas – Austin

1. Ge, Ruifang. Learning for semantic parsing using statistical syntactic parsing techniques.

Degree: PhD, Computer Sciences, 2010, University of Texas – Austin

 Natural language understanding is a sub-field of natural language processing, which builds automated systems to understand natural language. It is such an ambitious task that… (more)

Subjects/Keywords: Semantic parsing; Statistical syntactic parsing

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APA (6th Edition):

Ge, R. (2010). Learning for semantic parsing using statistical syntactic parsing techniques. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/26599

Chicago Manual of Style (16th Edition):

Ge, Ruifang. “Learning for semantic parsing using statistical syntactic parsing techniques.” 2010. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/26599.

MLA Handbook (7th Edition):

Ge, Ruifang. “Learning for semantic parsing using statistical syntactic parsing techniques.” 2010. Web. 13 Aug 2020.

Vancouver:

Ge R. Learning for semantic parsing using statistical syntactic parsing techniques. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2010. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/26599.

Council of Science Editors:

Ge R. Learning for semantic parsing using statistical syntactic parsing techniques. [Doctoral Dissertation]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/26599


University of Texas – Austin

2. Chaurasia, Shobhit. Dialog for natural language to code.

Degree: MSin Computer Sciences, Computer Science, 2017, University of Texas – Austin

 Generating computer code from natural language descriptions has been a long-standing problem in computational linguistics. Prior work in this domain has restricted itself to generating… (more)

Subjects/Keywords: Dialog; Interactive systems; Neural network; Deep learning; Semantic parsing; Computer code; Software engineering

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APA (6th Edition):

Chaurasia, S. (2017). Dialog for natural language to code. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62886

Chicago Manual of Style (16th Edition):

Chaurasia, Shobhit. “Dialog for natural language to code.” 2017. Masters Thesis, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/62886.

MLA Handbook (7th Edition):

Chaurasia, Shobhit. “Dialog for natural language to code.” 2017. Web. 13 Aug 2020.

Vancouver:

Chaurasia S. Dialog for natural language to code. [Internet] [Masters thesis]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/62886.

Council of Science Editors:

Chaurasia S. Dialog for natural language to code. [Masters Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62886

3. Yaghmazadeh, Navid. Automated synthesis of data extraction and transformation programs.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 Due to the abundance of data in today’s data-rich world, end-users increasingly need to perform various data extraction and transformation tasks. While many of these… (more)

Subjects/Keywords: Program synthesis; Programming-by-examples; Programming-by-natural-language; Databases

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APA (6th Edition):

Yaghmazadeh, N. (2017). Automated synthesis of data extraction and transformation programs. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68138

Chicago Manual of Style (16th Edition):

Yaghmazadeh, Navid. “Automated synthesis of data extraction and transformation programs.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/68138.

MLA Handbook (7th Edition):

Yaghmazadeh, Navid. “Automated synthesis of data extraction and transformation programs.” 2017. Web. 13 Aug 2020.

Vancouver:

Yaghmazadeh N. Automated synthesis of data extraction and transformation programs. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/68138.

Council of Science Editors:

Yaghmazadeh N. Automated synthesis of data extraction and transformation programs. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/68138

4. -4493-3358. Appropriate, accessible and appealing probabilistic graphical models.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 Appropriate - Many multivariate probabilistic models either use independent distributions or dependent Gaussian distributions. Yet, many real-world datasets contain count-valued or non-negative skewed data, e.g.… (more)

Subjects/Keywords: Graphical models; Topic models; Poisson; Count data; Visualization; Human computer interaction

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APA (6th Edition):

-4493-3358. (2017). Appropriate, accessible and appealing probabilistic graphical models. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62986

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-4493-3358. “Appropriate, accessible and appealing probabilistic graphical models.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/62986.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-4493-3358. “Appropriate, accessible and appealing probabilistic graphical models.” 2017. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-4493-3358. Appropriate, accessible and appealing probabilistic graphical models. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/62986.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-4493-3358. Appropriate, accessible and appealing probabilistic graphical models. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62986

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

5. Garrette, Daniel Hunter. Inducing grammars from linguistic universals and realistic amounts of supervision.

Degree: PhD, Artificial intelligence, 2015, University of Texas – Austin

 The best performing NLP models to date are learned from large volumes of manually-annotated data. For tasks like part-of-speech tagging and grammatical parsing, high performance… (more)

Subjects/Keywords: Computer science; Artificial intelligence; Natural language processing; Machine learning; Bayesian statistics; Grammar induction; Parsing; Computational linguistics

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APA (6th Edition):

Garrette, D. H. (2015). Inducing grammars from linguistic universals and realistic amounts of supervision. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/44478

Chicago Manual of Style (16th Edition):

Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/44478.

MLA Handbook (7th Edition):

Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Web. 13 Aug 2020.

Vancouver:

Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/44478.

Council of Science Editors:

Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/44478


University of Texas – Austin

6. Chen, Chao-Yeh. Learning human activities and poses with interconnected data sources.

Degree: PhD, Computer science, 2016, University of Texas – Austin

 Understanding human actions and poses in images or videos is a challenging problem in computer vision. There are different topics related to this problem such… (more)

Subjects/Keywords: Activity recognition; Activity detection

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APA (6th Edition):

Chen, C. (2016). Learning human activities and poses with interconnected data sources. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/40260

Chicago Manual of Style (16th Edition):

Chen, Chao-Yeh. “Learning human activities and poses with interconnected data sources.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/40260.

MLA Handbook (7th Edition):

Chen, Chao-Yeh. “Learning human activities and poses with interconnected data sources.” 2016. Web. 13 Aug 2020.

Vancouver:

Chen C. Learning human activities and poses with interconnected data sources. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/40260.

Council of Science Editors:

Chen C. Learning human activities and poses with interconnected data sources. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/40260


University of Texas – Austin

7. -7062-2970. Advances in statistical script learning.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 When humans encode information into natural language, they do so with the clear assumption that the reader will be able to seamlessly make inferences based… (more)

Subjects/Keywords: Natural language processing; Machine learning

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APA (6th Edition):

-7062-2970. (2017). Advances in statistical script learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63480

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-7062-2970. “Advances in statistical script learning.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/63480.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-7062-2970. “Advances in statistical script learning.” 2017. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7062-2970. Advances in statistical script learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/63480.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7062-2970. Advances in statistical script learning. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63480

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

8. -6301-1960. Explainable improved ensembling for natural language and vision.

Degree: PhD, Computer science, 2019, University of Texas – Austin

 Ensemble methods are well-known in machine learning for improving prediction accuracy. However, they do not adequately discriminate among underlying component models. The measure of how… (more)

Subjects/Keywords: Explainable AI; NLP; Computer Vision; Stacking

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APA (6th Edition):

-6301-1960. (2019). Explainable improved ensembling for natural language and vision. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/72820

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-6301-1960. “Explainable improved ensembling for natural language and vision.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/72820.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-6301-1960. “Explainable improved ensembling for natural language and vision.” 2019. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-6301-1960. Explainable improved ensembling for natural language and vision. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/72820.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-6301-1960. Explainable improved ensembling for natural language and vision. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://hdl.handle.net/2152/72820

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

9. Joshi, Shalmali Dilip. Constraint based approaches to interpretable and semi-supervised machine learning.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin

 Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social… (more)

Subjects/Keywords: Interpretable machine learning; Semi-supervised machine learning

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APA (6th Edition):

Joshi, S. D. (2019). Constraint based approaches to interpretable and semi-supervised machine learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1259

Chicago Manual of Style (16th Edition):

Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://dx.doi.org/10.26153/tsw/1259.

MLA Handbook (7th Edition):

Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Web. 13 Aug 2020.

Vancouver:

Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 13]. Available from: http://dx.doi.org/10.26153/tsw/1259.

Council of Science Editors:

Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1259

10. -3729-8456. Natural-language video description with deep recurrent neural networks.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 For most people, watching a brief video and describing what happened (in words) is an easy task. For machines, extracting meaning from video pixels and… (more)

Subjects/Keywords: Video; Captioning; Description; LSTM; RNN; Recurrent; Neural networks; Image captioning; Video captioning; Language and vision

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APA (6th Edition):

-3729-8456. (2017). Natural-language video description with deep recurrent neural networks. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62987

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-3729-8456. “Natural-language video description with deep recurrent neural networks.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/62987.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-3729-8456. “Natural-language video description with deep recurrent neural networks.” 2017. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3729-8456. Natural-language video description with deep recurrent neural networks. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/62987.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-3729-8456. Natural-language video description with deep recurrent neural networks. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62987

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

11. Jain, Suyog Dutt. Human machine collaboration for foreground segmentation in images and videos.

Degree: PhD, Computer Science, 2018, University of Texas – Austin

 Foreground segmentation is defined as the problem of generating pixel level foreground masks for all the objects in a given image or video. Accurate foreground… (more)

Subjects/Keywords: Computer vision; Crowdsourcing; Human machine collaboration; Image and video segmentation; Image segmentation; Video segmentation; Foreground segmentation; Object segmentation

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APA (6th Edition):

Jain, S. D. (2018). Human machine collaboration for foreground segmentation in images and videos. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63453

Chicago Manual of Style (16th Edition):

Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/63453.

MLA Handbook (7th Edition):

Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Web. 13 Aug 2020.

Vancouver:

Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/63453.

Council of Science Editors:

Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63453

12. Kim, Joo Hyun, active 2013. Grounded language learning models for ambiguous supervision.

Degree: PhD, Computer Science, 2013, University of Texas – Austin

 Communicating with natural language interfaces is a long-standing, ultimate goal for artificial intelligence (AI) agents to pursue, eventually. One core issue toward this goal is… (more)

Subjects/Keywords: Grounded language learning; Semantic parsing; Learning from ambiguous supervision; Probabilistic alignment; Natural language processing

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APA (6th Edition):

Kim, Joo Hyun, a. 2. (2013). Grounded language learning models for ambiguous supervision. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/22986

Chicago Manual of Style (16th Edition):

Kim, Joo Hyun, active 2013. “Grounded language learning models for ambiguous supervision.” 2013. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/22986.

MLA Handbook (7th Edition):

Kim, Joo Hyun, active 2013. “Grounded language learning models for ambiguous supervision.” 2013. Web. 13 Aug 2020.

Vancouver:

Kim, Joo Hyun a2. Grounded language learning models for ambiguous supervision. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2013. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/22986.

Council of Science Editors:

Kim, Joo Hyun a2. Grounded language learning models for ambiguous supervision. [Doctoral Dissertation]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/22986

13. Viswanathan, Vidhoon. Knowledge base population using stacked ensembles of information extractors.

Degree: MSin Computer Sciences, Computer Science, 2015, University of Texas – Austin

 The performance of relation extractors plays a significant role in automatic creation of knowledge bases from web corpus. Using automated systems to create knowledge bases… (more)

Subjects/Keywords: KBP; Slot filling; Information extraction

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APA (6th Edition):

Viswanathan, V. (2015). Knowledge base population using stacked ensembles of information extractors. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31849

Chicago Manual of Style (16th Edition):

Viswanathan, Vidhoon. “Knowledge base population using stacked ensembles of information extractors.” 2015. Masters Thesis, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/31849.

MLA Handbook (7th Edition):

Viswanathan, Vidhoon. “Knowledge base population using stacked ensembles of information extractors.” 2015. Web. 13 Aug 2020.

Vancouver:

Viswanathan V. Knowledge base population using stacked ensembles of information extractors. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/31849.

Council of Science Editors:

Viswanathan V. Knowledge base population using stacked ensembles of information extractors. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31849

14. Roller, Stephen Creig. Identifying lexical relationships and entailments with distributional semantics.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 Many modern efforts in Natural Language Understanding depend on rich and powerful semantic representations of words. Systems for sophisticated logical and textual reasoning often depend… (more)

Subjects/Keywords: Natural language processing; Lexical semantics; Lexical relationships; Hypernymy; Distributional semantics

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

APA (6th Edition):

Roller, S. C. (2017). Identifying lexical relationships and entailments with distributional semantics. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61528

Chicago Manual of Style (16th Edition):

Roller, Stephen Creig. “Identifying lexical relationships and entailments with distributional semantics.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/61528.

MLA Handbook (7th Edition):

Roller, Stephen Creig. “Identifying lexical relationships and entailments with distributional semantics.” 2017. Web. 13 Aug 2020.

Vancouver:

Roller SC. Identifying lexical relationships and entailments with distributional semantics. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/61528.

Council of Science Editors:

Roller SC. Identifying lexical relationships and entailments with distributional semantics. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61528

15. -9199-0633. Continually improving grounded natural language understanding through human-robot dialog.

Degree: PhD, Computer Science, 2018, University of Texas – Austin

 As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge… (more)

Subjects/Keywords: Natural language processing; Human-robot dialog

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APA (6th Edition):

-9199-0633. (2018). Continually improving grounded natural language understanding through human-robot dialog. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68120

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/68120.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/68120.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68120

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

16. Zhang, Ye, 1989-. Neural NLP models under low-supervision scenarios.

Degree: PhD, Computer Science, 2019, University of Texas – Austin

 Neural models have been shown to work well for natural language processing tasks when one has large amounts of labeled data, but problems arise when… (more)

Subjects/Keywords: Neural models; Natural language processing; Low-supervision

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APA (6th Edition):

Zhang, Ye, 1. (2019). Neural NLP models under low-supervision scenarios. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/2140

Chicago Manual of Style (16th Edition):

Zhang, Ye, 1989-. “Neural NLP models under low-supervision scenarios.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://dx.doi.org/10.26153/tsw/2140.

MLA Handbook (7th Edition):

Zhang, Ye, 1989-. “Neural NLP models under low-supervision scenarios.” 2019. Web. 13 Aug 2020.

Vancouver:

Zhang, Ye 1. Neural NLP models under low-supervision scenarios. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 13]. Available from: http://dx.doi.org/10.26153/tsw/2140.

Council of Science Editors:

Zhang, Ye 1. Neural NLP models under low-supervision scenarios. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/2140

17. https://orcid.org/0000-0002-8791-7828. Listwise frameworks for ranking and rank aggregation.

Degree: PhD, Computer Science, 2018, University of Texas – Austin

 The goal in Learning to Rank (LETOR) is to learn to order a novel set of items, given training data comprising sets of items and… (more)

Subjects/Keywords: Machine learning; Ranking; Learning to rank; Rank aggregation; Listwise methods; Tracking

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

APA (6th Edition):

https://orcid.org/0000-0002-8791-7828. (2018). Listwise frameworks for ranking and rank aggregation. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63694

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

https://orcid.org/0000-0002-8791-7828. “Listwise frameworks for ranking and rank aggregation.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/63694.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

https://orcid.org/0000-0002-8791-7828. “Listwise frameworks for ranking and rank aggregation.” 2018. Web. 13 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

https://orcid.org/0000-0002-8791-7828. Listwise frameworks for ranking and rank aggregation. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/63694.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

https://orcid.org/0000-0002-8791-7828. Listwise frameworks for ranking and rank aggregation. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63694

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

18. Tang, Lap Poon Rupert. Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining.

Degree: PhD, Computer Sciences, 2003, University of Texas – Austin

Subjects/Keywords: Logic programming; Natural language processing (Computer science); Data mining

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Tang, L. P. R. (2003). Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/986

Chicago Manual of Style (16th Edition):

Tang, Lap Poon Rupert. “Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining.” 2003. Doctoral Dissertation, University of Texas – Austin. Accessed August 13, 2020. http://hdl.handle.net/2152/986.

MLA Handbook (7th Edition):

Tang, Lap Poon Rupert. “Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining.” 2003. Web. 13 Aug 2020.

Vancouver:

Tang LPR. Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2003. [cited 2020 Aug 13]. Available from: http://hdl.handle.net/2152/986.

Council of Science Editors:

Tang LPR. Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining. [Doctoral Dissertation]. University of Texas – Austin; 2003. Available from: http://hdl.handle.net/2152/986

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