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You searched for +publisher:"University of Texas – Austin" +contributor:("Mooney, Raymond J. (Raymond Joseph)"). Showing records 1 – 25 of 25 total matches.

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

Degree: Computer Sciences, 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Kim, Joo Hyun, active 2013. “Grounded language learning models for ambiguous supervision.” 2013. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/22986.

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

MLA Handbook (7th Edition):

Kim, Joo Hyun, active 2013. “Grounded language learning models for ambiguous supervision.” 2013. Web. 19 Jun 2019.

Vancouver:

Kim, Joo Hyun a2. Grounded language learning models for ambiguous supervision. [Internet] [Thesis]. University of Texas – Austin; 2013. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/22986.

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

Council of Science Editors:

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

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


University of Texas – Austin

2. Reisinger, Joseph Simon. Latent variable models of distributional lexical semantics.

Degree: Computer Sciences, 2012, University of Texas – Austin

 In order to respond to increasing demand for natural language interfaces – and provide meaningful insight into user query intent – fast, scalable lexical semantic models with… (more)

Subjects/Keywords: Lexical semantics; Natural language processing; Machine learning

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

APA (6th Edition):

Reisinger, J. S. (2012). Latent variable models of distributional lexical semantics. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/26889

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

Chicago Manual of Style (16th Edition):

Reisinger, Joseph Simon. “Latent variable models of distributional lexical semantics.” 2012. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/26889.

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

MLA Handbook (7th Edition):

Reisinger, Joseph Simon. “Latent variable models of distributional lexical semantics.” 2012. Web. 19 Jun 2019.

Vancouver:

Reisinger JS. Latent variable models of distributional lexical semantics. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/26889.

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

Council of Science Editors:

Reisinger JS. Latent variable models of distributional lexical semantics. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/26889

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


University of Texas – Austin

3. Mihalkova, Lilyana Simeonova. Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation.

Degree: Computer Sciences, 2009, University of Texas – Austin

 Traditionally, machine learning algorithms assume that training data is provided as a set of independent instances, each of which can be described as a feature… (more)

Subjects/Keywords: Markov logic networks; Web query disambiguation; Statistical Relational Learning; Artificial intelligence; Machine learning; Multi-relational data; First-order logic; Probability distribution; Structure learning; Transfer learning; Algorithms

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APA (6th Edition):

Mihalkova, L. S. (2009). Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/10574

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

Chicago Manual of Style (16th Edition):

Mihalkova, Lilyana Simeonova. “Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation.” 2009. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/10574.

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

MLA Handbook (7th Edition):

Mihalkova, Lilyana Simeonova. “Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation.” 2009. Web. 19 Jun 2019.

Vancouver:

Mihalkova LS. Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/10574.

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

Council of Science Editors:

Mihalkova LS. Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/10574

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


University of Texas – Austin

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

Degree: 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. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/26599

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

Chicago Manual of Style (16th Edition):

Ge, Ruifang. “Learning for semantic parsing using statistical syntactic parsing techniques.” 2010. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/26599.

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

MLA Handbook (7th Edition):

Ge, Ruifang. “Learning for semantic parsing using statistical syntactic parsing techniques.” 2010. Web. 19 Jun 2019.

Vancouver:

Ge R. Learning for semantic parsing using statistical syntactic parsing techniques. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/26599.

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

Council of Science Editors:

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

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


University of Texas – Austin

5. Ramanujam, Srivatsan. Factorial Hidden Markov Models for full and weakly supervised supertagging.

Degree: Computer Sciences, 2009, University of Texas – Austin

 For many sequence prediction tasks in Natural Language Processing, modeling dependencies between individual predictions can be used to improve prediction accuracy of the sequence as… (more)

Subjects/Keywords: Hidden Markov Models; Bayesian Models; Categorial Grammar; Supertagging; Joint Inference

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APA (6th Edition):

Ramanujam, S. (2009). Factorial Hidden Markov Models for full and weakly supervised supertagging. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2009-08-350

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

Chicago Manual of Style (16th Edition):

Ramanujam, Srivatsan. “Factorial Hidden Markov Models for full and weakly supervised supertagging.” 2009. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/ETD-UT-2009-08-350.

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

MLA Handbook (7th Edition):

Ramanujam, Srivatsan. “Factorial Hidden Markov Models for full and weakly supervised supertagging.” 2009. Web. 19 Jun 2019.

Vancouver:

Ramanujam S. Factorial Hidden Markov Models for full and weakly supervised supertagging. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-350.

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

Council of Science Editors:

Ramanujam S. Factorial Hidden Markov Models for full and weakly supervised supertagging. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-350

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


University of Texas – Austin

6. Gupta, Sonal. Activity retrieval in closed captioned videos.

Degree: Computer Sciences, 2009, University of Texas – Austin

 Recognizing activities in real-world videos is a difficult problem exacerbated by background clutter, changes in camera angle & zoom, occlusion and rapid camera movements. Large… (more)

Subjects/Keywords: Activity Recognition; Action Recognition; Video Retrieval; Machine Learning; Computer Vision; Multimedia; Closed Captions

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

APA (6th Edition):

Gupta, S. (2009). Activity retrieval in closed captioned videos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2009-08-305

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

Chicago Manual of Style (16th Edition):

Gupta, Sonal. “Activity retrieval in closed captioned videos.” 2009. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/ETD-UT-2009-08-305.

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

MLA Handbook (7th Edition):

Gupta, Sonal. “Activity retrieval in closed captioned videos.” 2009. Web. 19 Jun 2019.

Vancouver:

Gupta S. Activity retrieval in closed captioned videos. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-305.

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

Council of Science Editors:

Gupta S. Activity retrieval in closed captioned videos. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-305

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


University of Texas – Austin

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

Degree: Computer Sciences, 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

Chaurasia, Shobhit. “Dialog for natural language to code.” 2017. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/62886.

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

MLA Handbook (7th Edition):

Chaurasia, Shobhit. “Dialog for natural language to code.” 2017. Web. 19 Jun 2019.

Vancouver:

Chaurasia S. Dialog for natural language to code. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/62886.

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

Council of Science Editors:

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

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

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

Degree: Computer Sciences, 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. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31849

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

Chicago Manual of Style (16th Edition):

Viswanathan, Vidhoon. “Knowledge base population using stacked ensembles of information extractors.” 2015. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/31849.

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

MLA Handbook (7th Edition):

Viswanathan, Vidhoon. “Knowledge base population using stacked ensembles of information extractors.” 2015. Web. 19 Jun 2019.

Vancouver:

Viswanathan V. Knowledge base population using stacked ensembles of information extractors. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/31849.

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

Council of Science Editors:

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

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


University of Texas – Austin

9. -9199-0633. Continually improving grounded natural language understanding through human-robot dialog.

Degree: Computer Sciences, 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/68120.

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

MLA Handbook (7th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Web. 19 Jun 2019.

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] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Jun 19]. 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
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Thesis]. 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
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

10. Huynh, Tuyen Ngoc. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Many real-world problems involve data that both have complex structures and uncertainty. Statistical relational learning (SRL) is an emerging area of research that addresses the… (more)

Subjects/Keywords: Markov logic networks; Statistical relational learning; Structured prediction; Logic networks; Artificial intelligence; Machine learning

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

APA (6th Edition):

Huynh, T. N. (2011). Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-05-3436

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

Chicago Manual of Style (16th Edition):

Huynh, Tuyen Ngoc. “Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.” 2011. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/ETD-UT-2011-05-3436.

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

MLA Handbook (7th Edition):

Huynh, Tuyen Ngoc. “Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.” 2011. Web. 19 Jun 2019.

Vancouver:

Huynh TN. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3436.

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

Council of Science Editors:

Huynh TN. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3436

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


University of Texas – Austin

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

Degree: Computer Sciences, 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. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/44478

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

Chicago Manual of Style (16th Edition):

Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/44478.

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

MLA Handbook (7th Edition):

Garrette, Daniel Hunter. “Inducing grammars from linguistic universals and realistic amounts of supervision.” 2015. Web. 19 Jun 2019.

Vancouver:

Garrette DH. Inducing grammars from linguistic universals and realistic amounts of supervision. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/44478.

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

Council of Science Editors:

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

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


University of Texas – Austin

12. -3729-8456. Natural-language video description with deep recurrent neural networks.

Degree: Computer Sciences, 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

-3729-8456. (2017). Natural-language video description with deep recurrent neural networks. (Thesis). 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
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-3729-8456. “Natural-language video description with deep recurrent neural networks.” 2017. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/62987.

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

MLA Handbook (7th Edition):

-3729-8456. “Natural-language video description with deep recurrent neural networks.” 2017. Web. 19 Jun 2019.

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] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Jun 19]. 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
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-3729-8456. Natural-language video description with deep recurrent neural networks. [Thesis]. 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
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

13. -7062-2970. Advances in statistical script learning.

Degree: Computer Sciences, 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. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63480

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

Chicago Manual of Style (16th Edition):

-7062-2970. “Advances in statistical script learning.” 2017. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/63480.

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

MLA Handbook (7th Edition):

-7062-2970. “Advances in statistical script learning.” 2017. Web. 19 Jun 2019.

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] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Jun 19]. 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
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-7062-2970. Advances in statistical script learning. [Thesis]. 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
Not specified: Masters Thesis or Doctoral Dissertation

14. -3023-4337. Knowledge transfer using latent variable models.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

 In several applications, scarcity of labeled data is a challenging problem that hinders the predictive capabilities of machine learning algorithms. Additionally, the distribution of the… (more)

Subjects/Keywords: Transfer learning; Multitask learning; Gamma process; Poisson factorization; Supervised topic model

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

APA (6th Edition):

-3023-4337. (2015). Knowledge transfer using latent variable models. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31414

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

Chicago Manual of Style (16th Edition):

-3023-4337. “Knowledge transfer using latent variable models.” 2015. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/31414.

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

MLA Handbook (7th Edition):

-3023-4337. “Knowledge transfer using latent variable models.” 2015. Web. 19 Jun 2019.

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

Vancouver:

-3023-4337. Knowledge transfer using latent variable models. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/31414.

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

Council of Science Editors:

-3023-4337. Knowledge transfer using latent variable models. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31414

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

15. Beltagy, Islam Kamel Ahmed. Natural language semantics using probabilistic logic.

Degree: Computer Sciences, 2016, University of Texas – Austin

 With better natural language semantic representations, computers can do more applications more efficiently as a result of better understanding of natural text. However, no single… (more)

Subjects/Keywords: NLP; Machine learning; Probabilistic logic; MLNs; PSL; Markov Logic Network; QA; RTE; STS

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

APA (6th Edition):

Beltagy, I. K. A. (2016). Natural language semantics using probabilistic logic. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46617

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

Chicago Manual of Style (16th Edition):

Beltagy, Islam Kamel Ahmed. “Natural language semantics using probabilistic logic.” 2016. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/46617.

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

MLA Handbook (7th Edition):

Beltagy, Islam Kamel Ahmed. “Natural language semantics using probabilistic logic.” 2016. Web. 19 Jun 2019.

Vancouver:

Beltagy IKA. Natural language semantics using probabilistic logic. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/46617.

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

Council of Science Editors:

Beltagy IKA. Natural language semantics using probabilistic logic. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46617

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


University of Texas – Austin

16. Bilenko, Mikhail Yuryevich. Learnable similarity functions and their application to record linkage and clustering.

Degree: Computer Sciences, 2006, University of Texas – Austin

 Many machine learning and data mining tasks depend on functions that estimate similarity between instances. Similarity computations are particularly important in clustering and information integration… (more)

Subjects/Keywords: Cluster analysis – Data processing; Pattern recognition systems; Machine learning

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

APA (6th Edition):

Bilenko, M. Y. (2006). Learnable similarity functions and their application to record linkage and clustering. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/2681

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

Chicago Manual of Style (16th Edition):

Bilenko, Mikhail Yuryevich. “Learnable similarity functions and their application to record linkage and clustering.” 2006. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/2681.

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

MLA Handbook (7th Edition):

Bilenko, Mikhail Yuryevich. “Learnable similarity functions and their application to record linkage and clustering.” 2006. Web. 19 Jun 2019.

Vancouver:

Bilenko MY. Learnable similarity functions and their application to record linkage and clustering. [Internet] [Thesis]. University of Texas – Austin; 2006. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/2681.

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

Council of Science Editors:

Bilenko MY. Learnable similarity functions and their application to record linkage and clustering. [Thesis]. University of Texas – Austin; 2006. Available from: http://hdl.handle.net/2152/2681

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


University of Texas – Austin

17. Bunescu, Razvan Constantin, 1975-. Learning for information extraction: from named entity recognition and disambiguation to relation extraction.

Degree: Computer Sciences, 2007, University of Texas – Austin

 Information Extraction, the task of locating textual mentions of specific types of entities and their relationships, aims at representing the information contained in text documents… (more)

Subjects/Keywords: Natural language processing (Computer science); Information storage and retrieval systems

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APA (6th Edition):

Bunescu, Razvan Constantin, 1. (2007). Learning for information extraction: from named entity recognition and disambiguation to relation extraction. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/3200

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

Chicago Manual of Style (16th Edition):

Bunescu, Razvan Constantin, 1975-. “Learning for information extraction: from named entity recognition and disambiguation to relation extraction.” 2007. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/3200.

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

MLA Handbook (7th Edition):

Bunescu, Razvan Constantin, 1975-. “Learning for information extraction: from named entity recognition and disambiguation to relation extraction.” 2007. Web. 19 Jun 2019.

Vancouver:

Bunescu, Razvan Constantin 1. Learning for information extraction: from named entity recognition and disambiguation to relation extraction. [Internet] [Thesis]. University of Texas – Austin; 2007. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/3200.

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

Council of Science Editors:

Bunescu, Razvan Constantin 1. Learning for information extraction: from named entity recognition and disambiguation to relation extraction. [Thesis]. University of Texas – Austin; 2007. Available from: http://hdl.handle.net/2152/3200

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


University of Texas – Austin

18. Kate, Rohit Jaivant, 1978-. Learning for semantic parsing with kernels under various forms of supervision.

Degree: Computer Sciences, 2007, University of Texas – Austin

 Semantic parsing involves deep semantic analysis that maps natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical… (more)

Subjects/Keywords: Parsing (Computer grammar); Machine learning; Natural language processing (Computer science)

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APA (6th Edition):

Kate, Rohit Jaivant, 1. (2007). Learning for semantic parsing with kernels under various forms of supervision. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/3272

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

Chicago Manual of Style (16th Edition):

Kate, Rohit Jaivant, 1978-. “Learning for semantic parsing with kernels under various forms of supervision.” 2007. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/3272.

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

MLA Handbook (7th Edition):

Kate, Rohit Jaivant, 1978-. “Learning for semantic parsing with kernels under various forms of supervision.” 2007. Web. 19 Jun 2019.

Vancouver:

Kate, Rohit Jaivant 1. Learning for semantic parsing with kernels under various forms of supervision. [Internet] [Thesis]. University of Texas – Austin; 2007. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/3272.

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

Council of Science Editors:

Kate, Rohit Jaivant 1. Learning for semantic parsing with kernels under various forms of supervision. [Thesis]. University of Texas – Austin; 2007. Available from: http://hdl.handle.net/2152/3272

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


University of Texas – Austin

19. Wong, Yuk Wah, 1979-. Learning for semantic parsing and natural language generation using statistical machine translation techniques.

Degree: Computer Sciences, 2007, University of Texas – Austin

 One of the main goals of natural language processing (NLP) is to build au- tomated systems that can understand and generate human lanugages. This goal… (more)

Subjects/Keywords: Natural language processing (Computer science); Parsing (Computer grammar)

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

APA (6th Edition):

Wong, Yuk Wah, 1. (2007). Learning for semantic parsing and natural language generation using statistical machine translation techniques. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/3351

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

Chicago Manual of Style (16th Edition):

Wong, Yuk Wah, 1979-. “Learning for semantic parsing and natural language generation using statistical machine translation techniques.” 2007. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/3351.

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

MLA Handbook (7th Edition):

Wong, Yuk Wah, 1979-. “Learning for semantic parsing and natural language generation using statistical machine translation techniques.” 2007. Web. 19 Jun 2019.

Vancouver:

Wong, Yuk Wah 1. Learning for semantic parsing and natural language generation using statistical machine translation techniques. [Internet] [Thesis]. University of Texas – Austin; 2007. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/3351.

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

Council of Science Editors:

Wong, Yuk Wah 1. Learning for semantic parsing and natural language generation using statistical machine translation techniques. [Thesis]. University of Texas – Austin; 2007. Available from: http://hdl.handle.net/2152/3351

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


University of Texas – Austin

20. Basu, Sugato. Semi-supervised clustering: probabilistic models, algorithms and experiments.

Degree: Computer Sciences, 2005, University of Texas – Austin

 Clustering is one of the most common data mining tasks, used frequently for data categorization and analysis in both industry and academia. The focus of… (more)

Subjects/Keywords: Cluster analysis; Computer algorithms

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APA (6th Edition):

Basu, S. (2005). Semi-supervised clustering: probabilistic models, algorithms and experiments. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/1820

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

Chicago Manual of Style (16th Edition):

Basu, Sugato. “Semi-supervised clustering: probabilistic models, algorithms and experiments.” 2005. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/1820.

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

MLA Handbook (7th Edition):

Basu, Sugato. “Semi-supervised clustering: probabilistic models, algorithms and experiments.” 2005. Web. 19 Jun 2019.

Vancouver:

Basu S. Semi-supervised clustering: probabilistic models, algorithms and experiments. [Internet] [Thesis]. University of Texas – Austin; 2005. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/1820.

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

Council of Science Editors:

Basu S. Semi-supervised clustering: probabilistic models, algorithms and experiments. [Thesis]. University of Texas – Austin; 2005. Available from: http://hdl.handle.net/2152/1820

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


University of Texas – Austin

21. Melville, Prem Noel. Creating diverse ensemble classifiers to reduce supervision.

Degree: Computer Sciences, 2005, University of Texas – Austin

 Ensemble methods like Bagging and Boosting which combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of… (more)

Subjects/Keywords: Machine learning

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

APA (6th Edition):

Melville, P. N. (2005). Creating diverse ensemble classifiers to reduce supervision. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/2295

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

Chicago Manual of Style (16th Edition):

Melville, Prem Noel. “Creating diverse ensemble classifiers to reduce supervision.” 2005. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/2295.

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

MLA Handbook (7th Edition):

Melville, Prem Noel. “Creating diverse ensemble classifiers to reduce supervision.” 2005. Web. 19 Jun 2019.

Vancouver:

Melville PN. Creating diverse ensemble classifiers to reduce supervision. [Internet] [Thesis]. University of Texas – Austin; 2005. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/2295.

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

Council of Science Editors:

Melville PN. Creating diverse ensemble classifiers to reduce supervision. [Thesis]. University of Texas – Austin; 2005. Available from: http://hdl.handle.net/2152/2295

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


University of Texas – Austin

22. 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: Computer Sciences, 2003, University of Texas – Austin

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

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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. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/986

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

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. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/986.

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

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. 19 Jun 2019.

Vancouver:

Tang LPR. Integrating top-down and bottom-up approaches in inductive logic programming: applications in natural language processing and relational data mining. [Internet] [Thesis]. University of Texas – Austin; 2003. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/986.

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

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. [Thesis]. University of Texas – Austin; 2003. Available from: http://hdl.handle.net/2152/986

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


University of Texas – Austin

23. Nahm, Un Yong. Text mining with information extraction.

Degree: Computer Sciences, 2004, University of Texas – Austin

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

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

APA (6th Edition):

Nahm, U. Y. (2004). Text mining with information extraction. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/1280

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

Chicago Manual of Style (16th Edition):

Nahm, Un Yong. “Text mining with information extraction.” 2004. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/1280.

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

MLA Handbook (7th Edition):

Nahm, Un Yong. “Text mining with information extraction.” 2004. Web. 19 Jun 2019.

Vancouver:

Nahm UY. Text mining with information extraction. [Internet] [Thesis]. University of Texas – Austin; 2004. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/1280.

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

Council of Science Editors:

Nahm UY. Text mining with information extraction. [Thesis]. University of Texas – Austin; 2004. Available from: http://hdl.handle.net/2152/1280

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

24. Vijaya Raghavan, Sindhu. Bayesian Logic Programs for plan recognition and machine reading.

Degree: Computer Sciences, 2012, University of Texas – Austin

 Several real world tasks involve data that is uncertain and relational in nature. Traditional approaches like first-order logic and probabilistic models either deal with structured… (more)

Subjects/Keywords: Bayesian Logic Programs; Statistical relational learning; Plan recognition; Abductive reasoning; Machine reading; Rule learning; Information extraction; BLPs; SRL; IE; BALPs; Bayesian Abductive Logic Programs

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

APA (6th Edition):

Vijaya Raghavan, S. (2012). Bayesian Logic Programs for plan recognition and machine reading. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/19544

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

Chicago Manual of Style (16th Edition):

Vijaya Raghavan, Sindhu. “Bayesian Logic Programs for plan recognition and machine reading.” 2012. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/19544.

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

MLA Handbook (7th Edition):

Vijaya Raghavan, Sindhu. “Bayesian Logic Programs for plan recognition and machine reading.” 2012. Web. 19 Jun 2019.

Vancouver:

Vijaya Raghavan S. Bayesian Logic Programs for plan recognition and machine reading. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/19544.

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

Council of Science Editors:

Vijaya Raghavan S. Bayesian Logic Programs for plan recognition and machine reading. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/19544

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

25. Chen, David Lieh-Chiang. Learning language from ambiguous perceptual context.

Degree: Computer Sciences, 2012, University of Texas – Austin

 Building a computer system that can understand human languages has been one of the long-standing goals of artificial intelligence. Currently, most state-of-the-art natural language processing… (more)

Subjects/Keywords: Natural language processing; Natural language learning; Connecting language and perception; Machine learning; Artificial intelligence

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

APA (6th Edition):

Chen, D. L. (2012). Learning language from ambiguous perceptual context. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5203

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

Chicago Manual of Style (16th Edition):

Chen, David Lieh-Chiang. “Learning language from ambiguous perceptual context.” 2012. Thesis, University of Texas – Austin. Accessed June 19, 2019. http://hdl.handle.net/2152/ETD-UT-2012-05-5203.

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

MLA Handbook (7th Edition):

Chen, David Lieh-Chiang. “Learning language from ambiguous perceptual context.” 2012. Web. 19 Jun 2019.

Vancouver:

Chen DL. Learning language from ambiguous perceptual context. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Jun 19]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5203.

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

Council of Science Editors:

Chen DL. Learning language from ambiguous perceptual context. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5203

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

.