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Infusing Automatic Question Generation with Natural Language Understanding
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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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| 4 |
Exploration of Visual, Acoustic, and Physiological Modalities to Complement Linguistic Representations for Sentiment Analysis
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| 5 |
Multilingual Word Sense Disambiguation Using Wikipedia
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Abstract:
Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. Word sense disambiguation is the task of automatically assigning the most appropriate meaning to a polysemous word within a given context. Generally the problem of resolving ambiguity in literature has revolved around the famous quote “you shall know the meaning of the word by the company it keeps.” In this thesis, we investigate the role of context for resolving ambiguity through three different approaches. Instead of using a predefined monolingual sense inventory such as WordNet, we use a language-independent framework where the word senses and sense-tagged data are derived automatically from Wikipedia. Using Wikipedia as a source of sense-annotations provides the much needed solution for knowledge acquisition bottleneck. In order to evaluate the viability of Wikipedia based sense-annotations, we cast the task of disambiguating polysemous nouns as a monolingual classification task and experimented on lexical samples from four different languages (viz. English, German, Italian and Spanish). The experiments confirm that the Wikipedia based sense annotations are reliable and can be used to construct accurate monolingual sense classifiers. It is a long belief that exploiting multiple languages helps in building accurate word sense disambiguation systems. Subsequently, we developed two approaches that recast the task of disambiguating polysemous nouns as a multilingual classification task. The first approach for multilingual word sense disambiguation attempts to effectively use a machine translation system to leverage two relevant multilingual aspects of the semantics of text. First, the various senses of a target word may be translated into different words, which constitute unique, yet highly salient signal that effectively expand the target word’s feature space. Second, the translated context words themselves embed co-occurrence information that a translation engine gathers from very large parallel corpora. The second approach for multlingual word sense disambiguation attempts to reduce the reliance on the machine translation system during training by using the multilingual knowledge available in Wikipedia through its interlingual links. Finally, the experiments on a lexical sample from four different languages confirm that the multilingual systems perform better than the monolingual system and significantly improve the disambiguation accuracy.
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Keyword:
multilingual; supervised learning; Wikipedia; word sense disambiguation
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URL: https://digital.library.unt.edu/ark:/67531/metadc500036/
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| 8 |
Finding Meaning in Context Using Graph Algorithms in Mono- and Cross-lingual Settings
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| 9 |
Sentence Similarity Analysis with Applications in Automatic Short Answer Grading
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| 10 |
Measuring Semantic Relatedness Using Salient Encyclopedic Concepts
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| 11 |
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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| 12 |
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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| 13 |
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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| 14 |
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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| 15 |
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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| 16 |
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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| 17 |
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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| 18 |
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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| 19 |
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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