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Infusing Automatic Question Generation with Natural Language Understanding
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Abstract:
Automatically generating questions from text for educational purposes is an active research area in natural language processing. The automatic question generation system accompanying this dissertation is MARGE, which is a recursive acronym for: MARGE automatically reads generates and evaluates. MARGE generates questions from both individual sentences and the passage as a whole, and is the first question generation system to successfully generate meaningful questions from textual units larger than a sentence. Prior work in automatic question generation from text treats a sentence as a string of constituents to be rearranged into as many questions as allowed by English grammar rules. Consequently, such systems overgenerate and create mainly trivial questions. Further, none of these systems to date has been able to automatically determine which questions are meaningful and which are trivial. This is because the research focus has been placed on NLG at the expense of NLU. In contrast, the work presented here infuses the questions generation process with natural language understanding. From the input text, MARGE creates a meaning analysis representation for each sentence in a passage via the DeconStructure algorithm presented in this work. Questions are generated from sentence meaning analysis representations using templates. The generated questions are automatically evaluated for question quality and importance via a ranking algorithm.
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Keyword:
Automatic question generation; Comparative and general -- Interrogative; Computational linguistics; Discourse analysis -- Data processing; Grammar
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URL: https://digital.library.unt.edu/ark:/67531/metadc955021/
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2 |
Automatic Language Identification for Metadata Records: Measuring the Effectiveness of Various Approaches
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3 |
Co-Training for Topic Classification of Scholarly Data
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In: 2015 Conference on Empirical Methods in Natural Language Processing, September 17-21, 2015. Lisbon, Portugal. (2015)
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Exploration of Visual, Acoustic, and Physiological Modalities to Complement Linguistic Representations for Sentiment Analysis
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Finding Meaning in Context Using Graph Algorithms in Mono- and Cross-lingual Settings
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Sentence Similarity Analysis with Applications in Automatic Short Answer Grading
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Measuring Semantic Relatedness Using Salient Encyclopedic Concepts
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Topic Modeling on Historical Newspapers
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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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Multilingual Subjectivity: Are More Languages Better?
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In: International Conference on Computational Linguistics (COLING), 2010, Beijing, China (2010)
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SemEval-2010 Task 2: Cross-Lingual Lexical Substitution
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In: Association for Computational Linguistics (ACL) Workshop on Semantic Evaluations (SemEval), 2010, Uppsala, Sweden (2010)
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Annotating and Identifying Emotions in Text
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In: Intelligent Information Access, 2010. Berlin: Springer-Verlag, v. 301/2010, pp. 21-38. (2010)
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Text Mining for Automatic Image Tagging
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In: Twenty-third Annual International Conference on Computational Linguistics (COLING), 2010, Beijing, China (2010)
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Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation
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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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Linguistic Ethnography: Identifying Dominant Word Classes in Text
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In: Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2009, Mexico City, Mexico (2009)
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Combining Lexical Resources for Contextual Synonym Expansion
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In: International Conference in Recent Advances in Natural Language Processing (RANLP), 2009, Borovets, Bulgaria (2009)
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The Decomposition of Human-Written Book Summaries
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In: Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2009, Mexico City, Mexico (2009)
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20 |
Subjectivity Word Sense Disambiguation
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In: Conference on Empirical Methods in Natural Language Processing (EMNLP), 2009, Singapore (2009)
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