Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: Natural Language Processing

Natural Language Processing is a broad area that includes various approaches to building computational systems that understand and generate language, as well as categorization and analysis of text documents, and cognitive models of human language processing.

Sub-areas:

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  1. Sparse Meets Dense: A Hybrid Approach to Enhance Scientific Document Retrieval
    [Details] [PDF]
    Priyanka Mandikal, Raymond Mooney
    In The 4th Workshop on Scientific Document Understanding, AAAI, February 2024.
  2. “Female Astronaut: Because sandwiches won’t make themselves up there!": Towards multi-modal misogyny detection in memes
    [Details] [PDF]
    Smriti Singh, Amritha Haridasan, Raymond Mooney
    Association of Computational Linguistics (ACL), Workshop on Online Abuse and Harms (WOAH), July 2023.
  3. Using Commonsense Knowledge to Answer Why-Questions
    [Details] [PDF] [Video]
    Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
    In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, December 2022.
  4. Updated Headline Generation: Creating Updated Summaries for Evolving News Stories
    [Details] [PDF] [Slides (PDF)] [Poster] [Video]
    Sheena Panthaplackel, Adrian Benton, Mark Dredze
    In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, May 2022.
  5. TellMeWhy: A Dataset for Answering Why-Questions in Narratives
    [Details] [PDF] [Slides (PDF)] [Video]
    Yash Kumar Lal, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
    In Findings of ACL 2021, August 2021.
  6. Facilitating Software Evolution through Natural Language Comments and Dialogue
    [Details] [PDF] [Slides (PDF)] [Video]
    Sheena Panthaplackel
    October 2021. Ph.D. Proposal.
  7. Using Natural Language to Aid Task Specification in Sequential Decision Making Problems
    [Details] [PDF] [Slides (PDF)] [Video]
    Prasoon Goyal
    October 2021. Ph.D. Proposal.
  8. Dialog as a Vehicle for Lifelong Learning
    [Details] [PDF] [Slides (PDF)] [Video]
    Aishwarya Padmakumar, Raymond J. Mooney
    In Position Paper Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDial 2.0), July 2020.
  9. Evaluating the Robustness of Natural Language Reward Shaping Models to Spatial Relations
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    Antony Yun
    May 2020. Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
  10. Using Natural Language for Reward Shaping in Reinforcement Learning
    [Details] [PDF] [Slides (PDF)] [Poster]
    Prasoon Goyal, Scott Niekum, Raymond J. Mooney
    In Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, August 2019.
  11. Explainable Improved Ensembling for Natural Language and Vision
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    Nazneen Rajani
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, July 2018.
  12. Distributional modeling on a diet: One-shot word learning from text only
    [Details] [PDF]
    Su Wang and Stephen Roller and Katrin Erk
    In In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), Taipei, Taiwan, November 2017.
  13. Dialog for Language to Code
    [Details] [PDF] [Poster]
    Shobhit Chaurasia and Raymond J. Mooney
    In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), 175-180, Taipei, Taiwan, November 2017.
  14. Leveraging Discourse Information Effectively for Authorship Attribution
    [Details] [PDF] [Slides (PDF)] [Video]
    Elisa Ferracane and Su Wang and Raymond J. Mooney
    In In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), 584–593, Taipei, Taiwan, November 2017.
  15. Natural-Language Video Description with Deep Recurrent Neural Networks
    [Details] [PDF] [Slides (PDF)]
    Subhashini Venugopalan
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, August 2017.
  16. Advances in Statistical Script Learning
    [Details] [PDF] [Slides (PPT)]
    Karl Pichotta
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, August 2017.
  17. Dialog for Natural Language to Code
    [Details] [PDF]
    Shobhit Chaurasia
    2017. Masters Thesis, Computer Science Department, University of Texas at Austin.
  18. Guiding Interaction Behaviors for Multi-modal Grounded Language Learning
    [Details] [PDF]
    Jesse Thomason and Jivko Sinapov and Raymond J. Mooney
    In Proceedings of the Workshop on Language Grounding for Robotics at ACL 2017 (RoboNLP-17), Vancouver, Canada, August 2017.
  19. Multi-Modal Word Synset Induction
    [Details] [PDF]
    Jesse Thomason and Raymond J. Mooney
    In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), 4116--4122, Melbourne, Australia, 2017.
  20. Stacking With Auxiliary Features
    [Details] [PDF] [Slides (PDF)] [Poster]
    Nazneen Fatema Rajani and Raymond J. Mooney
    In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), 2634-2640, Melbourne, Australia, 2017.
  21. Integrated Learning of Dialog Strategies and Semantic Parsing
    [Details] [PDF]
    Aishwarya Padmakumar and Jesse Thomason and Raymond J. Mooney
    In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), 547--557, Valencia, Spain, April 2017.
  22. Captioning Images with Diverse Objects
    [Details] [PDF] [Slides (PDF)] [Poster]
    Subhashini Venugopalan and Lisa Anne Hendricks and Marcus Rohrbach and Raymond Mooney and Trevor Darrell and Kate Saenko
    In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR-17), 5753--5761, 2017.
  23. Stacking With Auxiliary Features for Combining Supervised and Unsupervised Ensembles
    [Details] [PDF]
    Nazneen Fatema Rajani and Raymond J. Mooney
    In Proceedings of the Ninth Text Analysis Conference (TAC 2016), 2016.
  24. Stacking With Auxiliary Features: Improved Ensembling for Natural Language and Vision
    [Details] [PDF] [Slides (PDF)]
    Nazneen Fatema Rajani
    November 2016. PhD proposal, Department of Computer Science, The University of Texas at Austin.
  25. An Analysis of Using Semantic Parsing for Speech Recognition
    [Details] [PDF] [Slides (PPT)]
    Rodolfo Corona
    2016. Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
  26. Continuously Improving Natural Language Understanding for Robotic Systems through Semantic Parsing, Dialog, and Multi-modal Perception
    [Details] [PDF]
    Jesse Thomason
    November 2016. PhD proposal, Department of Computer Science, The University of Texas at Austin.
  27. Natural Language Semantics Using Probabilistic Logic
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    I. Beltagy
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, December 2016.
  28. Statistical Script Learning with Recurrent Neural Networks
    [Details] [PDF] [Poster]
    Karl Pichotta and Raymond J. Mooney
    In Proceedings of the Workshop on Uphill Battles in Language Processing (UBLP) at EMNLP 2016, Austin, TX, November 2016.
  29. PIC a Different Word: A Simple Model for Lexical Substitution in Context
    [Details] [PDF]
    Stephen Roller and Katrin Erk
    In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-16), 1121-1126, San Diego, California, 2016.
  30. MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification
    [Details] [PDF]
    Ye Zhang and Stephen Roller and Byron Wallace.
    In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-16), 1522--1527, San Diego, California, 2016.
  31. Improved Semantic Parsers For If-Then Statements
    [Details] [PDF]
    I. Beltagy and Chris Quirk
    To Appear In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL-16), Berlin, Germany, 2016.
  32. Combining Supervised and Unsupervised Ensembles for Knowledge Base Population
    [Details] [PDF]
    Nazneen Fatema Rajani and Raymond J. Mooney
    To Appear In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-16), 2016.
  33. Using Sentence-Level LSTM Language Models for Script Inference
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    Karl Pichotta and Raymond J. Mooney
    In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL-16), 279--289, Berlin, Germany, 2016.
  34. Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
    [Details] [PDF] [Poster]
    Subhashini Venugopalan and Lisa Anne Hendricks and Raymond Mooney and Kate Saenko
    In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-16), 1961--1966, Austin, Texas, 2016.
  35. Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
    [Details] [PDF]
    Lisa Anne Hendricks and Subhashini Venugopalan and Marcus Rohrbach and Raymond Mooney and Kate Saenko and Trevor Darrell
    In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR-16), 1--10, 2016.
  36. Learning Statistical Scripts with LSTM Recurrent Neural Networks
    [Details] [PDF] [Slides (PPT)] [Slides (PDF)]
    Karl Pichotta and Raymond J. Mooney
    In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona, February 2016.
  37. Representing Meaning with a Combination of Logical and Distributional Models
    [Details] [PDF]
    I. Beltagy and Stephen Roller and Pengxiang Cheng and Katrin Erk and Raymond J. Mooney
    The special issue of Computational Linguistics on Formal Distributional Semantics, 42(4), 2016.
  38. Stacked Ensembles of Information Extractors for Knowledge-Base Population by Combining Supervised and Unsupervised Approaches
    [Details] [PDF] [Slides (PDF)]
    Nazneen Fatema Rajani and Raymond J Mooney
    In Proceedings of the Eighth Text Analysis Conference (TAC 2015), November 2015.
  39. Statistical Script Learning with Recurrent Neural Nets
    [Details] [PDF] [Slides (PDF)]
    Karl Pichotta
    December 2015. PhD proposal, Department of Computer Science, The University of Texas at Austin.
  40. Natural Language Video Description using Deep Recurrent Neural Networks
    [Details] [PDF] [Slides (PDF)]
    Subhashini Venugopalan
    November 2015. PhD proposal, Department of Computer Science, The University of Texas at Austin.
  41. Inducing Grammars from Linguistic Universals and Realistic Amounts of Supervision
    [Details] [PDF]
    Dan Garrette
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, 2015.
  42. A Supertag-Context Model for Weakly-Supervised CCG Parser Learning
    [Details] [PDF] [Slides (PDF)]
    Dan Garrette and Chris Dyer and Jason Baldridge and Noah A. Smith
    In Proceedings of the 2015 Conference on Computational Natural Language Learning (CoNLL-2015), 22--31, Beijing, China, 2015.
  43. Sequence to Sequence -- Video to Text
    [Details] [PDF]
    Subhashini Venugopalan and Marcus Rohrbach and Jeff Donahue and Raymond J. Mooney and Trevor Darrell and Kate Saenko
    In Proceedings of the 2015 International Conference on Computer Vision (ICCV-15), Santiago, Chile, December 2015.
  44. Stacked Ensembles of Information Extractors for Knowledge-Base Population
    [Details] [PDF] [Slides (PPT)]
    Vidhoon Viswanathan and Nazneen Fatema Rajani and Yinon Bentor and Raymond J. Mooney
    In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL-15), 177-187, Beijing, China, July 2015.
  45. Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes
    [Details] [PDF] [Poster]
    Chris Quirk and Raymond Mooney and Michel Galley
    In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL-15), 878--888, Beijing, China, July 2015.
  46. Knowledge Base Population using Stacked Ensembles of Information Extractors
    [Details] [PDF]
    Vidhoon Viswanathan
    Masters Thesis, Department of Computer Science, The University of Texas at Austin, May 2015.
  47. Translating Videos to Natural Language Using Deep Recurrent Neural Networks
    [Details] [PDF] [Slides (PDF)]
    Subhashini Venugopalan and Huijuan Xu and Jeff Donahue and Marcus Rohrbach and Raymond Mooney and Kate Saenko
    In Proceedings the 2015 Conference of the North American Chapter of the Association for Computational Linguistics -- Human Language Technologies (NAACL HLT 2015), 1494--1504, Denver, Colorado, June 2015.
  48. Unsupervised Code-Switching for Multilingual Historical Document Transcription
    [Details] [PDF] [Slides (PDF)]
    Dan Garrette and Hannah Alpert-Abrams and Taylor Berg-Kirkpatrick and Dan Klein
    In Proceedings the 2015 Conference of the North American Chapter of the Association for Computational Linguistics -- Human Language Technologies (NAACL HLT 2015), 1036--1041, Denver, Colorado, June 2015.
  49. On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics
    [Details] [PDF] [Slides (PPT)]
    I. Beltagy and Katrin Erk
    In Proceedings of the 11th International Conference on Computational Semantics (IWCS-2015), London, UK, April 2015.
  50. Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning
    [Details] [PDF] [Slides (PDF)]
    Dan Garrette, Chris Dyer, Jason Baldridge, Noah A. Smith
    In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), Austin, TX, January 2015.
  51. Natural Language Semantics using Probabilistic Logic
    [Details] [PDF] [Slides (PPT)]
    I. Beltagy
    October 2014. PhD proposal, Department of Computer Science, The University of Texas at Austin.
  52. Weakly-Supervised Bayesian Learning of a CCG Supertagger
    [Details] [PDF] [Slides (PDF)] [Poster]
    Dan Garrette and Chris Dyer and Jason Baldridge and Noah A. Smith
    In Proceedings of the Eighteenth Conference on Computational Natural Language Learning (CoNLL-2014), 141--150, Baltimore, MD, June 2014.
  53. Inclusive yet Selective: Supervised Distributional Hypernymy Detection
    [Details] [PDF]
    Stephen Roller and Katrin Erk and Gemma Boleda
    In Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), 1025--1036, Dublin, Ireland, August 2014.
  54. UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic
    [Details] [PDF]
    I. Beltagy and Stephen Roller and Gemma Boleda and and Katrin Erk and Raymond J. Mooney
    In The 8th Workshop on Semantic Evaluation (SemEval-2014), 796--801, Dublin, Ireland, August 2014.
  55. Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild
    [Details] [PDF]
    Jesse Thomason and Subhashini Venugopalan and Sergio Guadarrama and Kate Saenko and Raymond Mooney
    In Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), 1218--1227, Dublin, Ireland, August 2014.
  56. Efficient Markov Logic Inference for Natural Language Semantics
    [Details] [PDF] [Poster]
    I. Beltagy and Raymond J. Mooney
    In Proceedings of the Fourth International Workshop on Statistical Relational AI at AAAI (StarAI-2014), 9--14, Quebec City, Canada, July 2014.
  57. Integrating Visual and Linguistic Information to Describe Properties of Objects
    [Details] [PDF]
    Calvin MacKenzie
    2014. Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
  58. Semantic Parsing using Distributional Semantics and Probabilistic Logic
    [Details] [PDF] [Poster]
    I. Beltagy and Katrin Erk and Raymond Mooney
    In Proceedings of ACL 2014 Workshop on Semantic Parsing (SP-2014), 7--11, Baltimore, MD, June 2014.
  59. Probabilistic Soft Logic for Semantic Textual Similarity
    [Details] [PDF] [Poster]
    I. Beltagy and Katrin Erk and Raymond J. Mooney
    In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL-14), 1210--1219, Baltimore, MD, 2014.
  60. Statistical Script Learning with Multi-Argument Events
    [Details] [PDF] [Poster]
    Karl Pichotta and Raymond J. Mooney
    In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014), 220--229, Gothenburg, Sweden, April 2014.
  61. University of Texas at Austin KBP 2013 Slot Filling System: Bayesian Logic Programs for Textual Inference
    [Details] [PDF]
    Yinon Bentor and Amelia Harrison and Shruti Bhosale and Raymond Mooney
    In Proceedings of the Sixth Text Analysis Conference (TAC 2013), 2013.
  62. YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-shot Recognition
    [Details] [PDF] [Poster]
    Sergio Guadarrama, Niveda Krishnamoorthy, Girish Malkarnenkar, Subhashini Venugopalan, Raymond Mooney, Trevor Darrell, Kate Saenko
    In Proceedings of the 14th International Conference on Computer Vision (ICCV-2013), 2712--2719, Sydney, Australia, December 2013.
  63. A Multimodal LDA Model Integrating Textual, Cognitive and Visual Modalities
    [Details] [PDF]
    Stephen Roller and Sabine Schulte im Walde
    In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), 1146--1157, Seattle, WA, October 2013.
  64. Identifying Phrasal Verbs Using Many Bilingual Corpora
    [Details] [PDF] [Poster]
    Karl Pichotta and John DeNero
    In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), 636--646, Seattle, WA, October 2013.
  65. Detecting Promotional Content in Wikipedia
    [Details] [PDF] [Slides (PPT)]
    Shruti Bhosale and Heath Vinicombe and Raymond J. Mooney
    In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), 1851--1857, Seattle, WA, October 2013.
  66. Grounded Language Learning Models for Ambiguous Supervision
    [Details] [PDF] [Slides (PPT)]
    Joo Hyun Kim
    PhD Thesis, Department of Computer Science, University of Texas at Austin, December 2013.
  67. Real-World Semi-Supervised Learning of POS-Taggers for Low-Resource Languages
    [Details] [PDF]
    Dan Garrette and Jason Mielens and Jason Baldridge
    In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013), 583--592, Sofia, Bulgaria, August 2013.
  68. Online Inference-Rule Learning from Natural-Language Extractions
    [Details] [PDF] [Poster]
    Sindhu Raghavan and Raymond J. Mooney
    In Proceedings of the 3rd Statistical Relational AI (StaRAI-13) workshop at AAAI '13, July 2013.
  69. Adapting Discriminative Reranking to Grounded Language Learning
    [Details] [PDF] [Slides (PPT)]
    Joohyun Kim and Raymond J. Mooney
    In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013), 218--227, Sofia, Bulgaria, August 2013.
  70. Montague Meets Markov: Deep Semantics with Probabilistic Logical Form
    [Details] [PDF] [Slides (PPT)]
    I. Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond Mooney
    In Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*Sem-2013), 11--21, Atlanta, GA, June 2013.
  71. A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics
    [Details] [PDF]
    Dan Garrette, Katrin Erk, Raymond J. Mooney
    In Harry Bunt, Johan Bos, and Stephen Pulman, editors, Computing Meaning, 27--48, Berlin, 2013. Springer.
  72. Learning a Part-of-Speech Tagger from Two Hours of Annotation
    [Details] [PDF] [Slides (PDF)] [Video]
    Dan Garrette, Jason Baldridge
    In Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-13), 138--147, Atlanta, GA, June 2013.
  73. Generating Natural-Language Video Descriptions Using Text-Mined Knowledge
    [Details] [PDF] [Slides (PPT)]
    Niveda Krishnamoorthy, Girish Malkarnenkar, Raymond J. Mooney, Kate Saenko, Sergio Guadarrama
    In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI-2013), 541--547, July 2013.
  74. Latent Variable Models of Distributional Lexical Semantics
    [Details] [PDF]
    Joseph Reisinger
    PhD Thesis, Department of Computer Science, University of Texas at Austin, May 2012.
  75. Bayesian Logic Programs for Plan Recognition and Machine Reading
    [Details] [PDF] [Slides (PPT)]
    Sindhu Raghavan
    PhD Thesis, Department of Computer Science, University of Texas at Austin, December 2012. 170.
  76. Type-Supervised Hidden Markov Models for Part-of-Speech Tagging with Incomplete Tag Dictionaries
    [Details] [PDF]
    Dan Garrette and Jason Baldridge
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012), 821--831, Jeju, Korea, July 2012.
  77. Improving Video Activity Recognition using Object Recognition and Text Mining
    [Details] [PDF] [Slides (PPT)]
    Tanvi S. Motwani and Raymond J. Mooney
    In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI-2012), 600--605, August 2012.
  78. Generative Models of Grounded Language Learning with Ambiguous Supervision
    [Details] [PDF] [Slides (PPT)]
    Joohyun Kim
    Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin, June 2012.
  79. Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision
    [Details] [PDF]
    Joohyun Kim and Raymond J. Mooney
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Natural Language Learning (EMNLP-CoNLL '12), 433--444, Jeju Island, Korea, July 2012.
  80. Fast Online Lexicon Learning for Grounded Language Acquisition
    [Details] [PDF] [Slides (PPT)]
    David L. Chen
    In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012), 430--439, July 2012.
  81. Learning Language from Ambiguous Perceptual Context
    [Details] [PDF] [Slides (PPT)]
    David L. Chen
    PhD Thesis, Department of Computer Science, University of Texas at Austin, May 2012. 196.
  82. Building a Persistent Workforce on Mechanical Turk for Multilingual Data Collection
    [Details] [PDF] [Slides (PPT)]
    David L. Chen and William B. Dolan
    In Proceedings of The 3rd Human Computation Workshop (HCOMP 2011), August 2011.
  83. Panning for Gold: Finding Relevant Semantic Content for Grounded Language Learning
    [Details] [PDF] [Slides (PDF)]
    David L. Chen and Raymond J. Mooney
    In Proceedings of Symposium on Machine Learning in Speech and Language Processing (MLSLP 2011), June 2011.
  84. Fine-Grained Class Label Markup of Search Queries
    [Details] [PDF]
    Joseph Reisinger and Marius Pasca
    In Proceedings of The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT 2011), 1200-1209, June 2011.
  85. Collecting Highly Parallel Data for Paraphrase Evaluation
    [Details] [PDF] [Slides (PPT)]
    David L. Chen and William B. Dolan
    In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, 190-200, Portland, Oregon, USA, June 2011.
  86. Integrating Logical Representations with Probabilistic Information using Markov Logic
    [Details] [PDF] [Slides (PDF)]
    Dan Garrette, Katrin Erk, Raymond Mooney
    In Proceedings of the International Conference on Computational Semantics, 105--114, Oxford, England, January 2011.
  87. Learning to Predict Readability using Diverse Linguistic Features
    [Details] [PDF] [Slides (PPT)]
    Rohit J. Kate, Xiaoqiang Luo, Siddharth Patwardhan, Martin Franz, Radu Florian, Raymond J. Mooney, Salim Roukos and Chris Welty
    In 23rd International Conference on Computational Linguistics (COLING 2010), 2010.
  88. Authorship Attribution Using Probabilistic Context-Free Grammars
    [Details] [PDF] [Slides (PPT)]
    Sindhu Raghavan, Adriana Kovashka and Raymond Mooney
    In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL-2010), 38--42, 2010.
  89. Associative Anaphora Resolution: A Web-Based Approach
    [Details] [PDF]
    Razvan Bunescu
    In Proceedings of the EACL-2003 Workshop on the Computational Treatment of Anaphora, 47-52, Budapest, Hungary, 2003.
  90. Machine Learning
    [Details] [PDF]
    Raymond J. Mooney
    New York, NY, 2003. McGraw-Hill.
  91. Learning Parse and Translation Decisions From Examples With Rich Context
    [Details] [PDF]
    Ulf Hermjakob and Raymond J. Mooney
    In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL'97/EACL'97), 482-489, July 1997.
  92. Learning Parse and Translation Decisions From Examples With Rich Context
    [Details] [PDF]
    Ulf Hermjakob
    PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, May 1997. 175 pages. Technical Report UT-AI97-261.
  93. Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Stefan Wermter and Ellen Riloff and Gabriela Scheler, editors, Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, 355-369, Berlin, 1996. Springer.
  94. Learning the Past Tense of English Verbs Using Inductive Logic Programming
    [Details] [PDF]
    Raymond J. Mooney and Mary Elaine Califf
    In {S. Wermter, E. Riloff} and G. Scheler, editors, Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, 370-384, Berlin, 1996. Springer.
  95. A General Explanation-Based Learning Mechanism and its Application to Narrative Understanding
    [Details] [PDF]
    Raymond J. Mooney
    Ph.D. thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, 1988
  96. Generalizing Explanations of Narratives into Schemata
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the Third International Machine Learning Workshop, 126--128, New Brunswick, New Jersey, 1985.
  97. Learning Schemata for Natural Language Processing
    [Details] [PDF]
    Raymond J. Mooney and Gerald F. DeJong
    In Proceedings of the Ninth International Joint Conference on Artificial Intelligence (IJCAI-85), 681-687, Los Angeles, CA, August 1985.
  98. Generalizing Explanations of Narratives into Schemata
    [Details] [PDF]
    Raymond J. Mooney
    Masters Thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, 1985.