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1
Infusing Automatic Question Generation with Natural Language Understanding
Mazidi, Karen. - : University of North Texas, 2016
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2
Automatic Language Identification for Metadata Records: Measuring the Effectiveness of Various Approaches
Knudson, Ryan Charles. - : University of North Texas, 2015
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3
Co-Training for Topic Classification of Scholarly Data
In: 2015 Conference on Empirical Methods in Natural Language Processing, September 17-21, 2015. Lisbon, Portugal. (2015)
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4
Exploration of Visual, Acoustic, and Physiological Modalities to Complement Linguistic Representations for Sentiment Analysis
Pérez-Rosas, Verónica. - : University of North Texas, 2014
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5
Multilingual Word Sense Disambiguation Using Wikipedia
Dandala, Bharath. - : University of North Texas, 2013
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6
Modeling Alcohol Consumption Using Blog Data
Koh, Kok Chuan. - : University of North Texas, 2013
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7
Extrapolating Subjectivity Research to Other Languages
Banea, Carmen. - : University of North Texas, 2013
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8
Finding Meaning in Context Using Graph Algorithms in Mono- and Cross-lingual Settings
Sinha, Ravi Som. - : University of North Texas, 2013
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9
Sentence Similarity Analysis with Applications in Automatic Short Answer Grading
Mohler, Michael A. G.. - : University of North Texas, 2012
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10
Measuring Semantic Relatedness Using Salient Encyclopedic Concepts
Hassan, Samer. - : University of North Texas, 2011
Abstract: While pragmatics, through its integration of situational awareness and real world relevant knowledge, offers a high level of analysis that is suitable for real interpretation of natural dialogue, semantics, on the other end, represents a lower yet more tractable and affordable linguistic level of analysis using current technologies. Generally, the understanding of semantic meaning in literature has revolved around the famous quote ``You shall know a word by the company it keeps''. In this thesis we investigate the role of context constituents in decoding the semantic meaning of the engulfing context; specifically we probe the role of salient concepts, defined as content-bearing expressions which afford encyclopedic definitions, as a suitable source of semantic clues to an unambiguous interpretation of context. Furthermore, we integrate this world knowledge in building a new and robust unsupervised semantic model and apply it to entail semantic relatedness between textual pairs, whether they are words, sentences or paragraphs. Moreover, we explore the abstraction of semantics across languages and utilize our findings into building a novel multi-lingual semantic relatedness model exploiting information acquired from various languages. We demonstrate the effectiveness and the superiority of our mono-lingual and multi-lingual models through a comprehensive set of evaluations on specialized synthetic datasets for semantic relatedness as well as real world applications such as paraphrase detection and short answer grading. Our work represents a novel approach to integrate world-knowledge into current semantic models and a means to cross the language boundary for a better and more robust semantic relatedness representation, thus opening the door for an improved abstraction of meaning that carries the potential of ultimately imparting understanding of natural language to machines.
Keyword: explicit semantic analysis; latent semantic analysis; salient semantic analysis; semantic relatedness; sematic similarity; Wikipedia
URL: http://digital.library.unt.edu/ark:/67531/metadc84212/
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11
Topic Modeling on Historical Newspapers
In: Association for Computational Linguistics (ACL) Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LATECH), 2011, Portland, Oregon, United States (2011)
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12
Multilingual Subjectivity: Are More Languages Better?
In: International Conference on Computational Linguistics (COLING), 2010, Beijing, China (2010)
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13
SemEval-2010 Task 2: Cross-Lingual Lexical Substitution
In: Association for Computational Linguistics (ACL) Workshop on Semantic Evaluations (SemEval), 2010, Uppsala, Sweden (2010)
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14
Annotating and Identifying Emotions in Text
In: Intelligent Information Access, 2010. Berlin: Springer-Verlag, v. 301/2010, pp. 21-38. (2010)
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15
Text Mining for Automatic Image Tagging
In: Twenty-third Annual International Conference on Computational Linguistics (COLING), 2010, Beijing, China (2010)
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16
Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation
In: North American Chapter of the Association for Computational Linguistics Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk, 2010, Los Angeles, California, United States (2010)
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17
Linguistic Ethnography: Identifying Dominant Word Classes in Text
In: Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2009, Mexico City, Mexico (2009)
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18
Combining Lexical Resources for Contextual Synonym Expansion
In: International Conference in Recent Advances in Natural Language Processing (RANLP), 2009, Borovets, Bulgaria (2009)
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19
The Decomposition of Human-Written Book Summaries
In: Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2009, Mexico City, Mexico (2009)
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20
Subjectivity Word Sense Disambiguation
In: Conference on Empirical Methods in Natural Language Processing (EMNLP), 2009, Singapore (2009)
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