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Finding Meaning in Context Using Graph Algorithms in Mono- and Cross-lingual Settings
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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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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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Graph-based Centrality Algorithms for Unsupervised Word Sense Disambiguation
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Abstract:
This thesis introduces an innovative methodology of combining some traditional dictionary based approaches to word sense disambiguation (semantic similarity measures and overlap of word glosses, both based on WordNet) with some graph-based centrality methods, namely the degree of the vertices, Pagerank, closeness, and betweenness. The approach is completely unsupervised, and is based on creating graphs for the words to be disambiguated. We experiment with several possible combinations of the semantic similarity measures as the first stage in our experiments. The next stage attempts to score individual vertices in the graphs previously created based on several graph connectivity measures. During the final stage, several voting schemes are applied on the results obtained from the different centrality algorithms. The most important contributions of this work are not only that it is a novel approach and it works well, but also that it has great potential in overcoming the new-knowledge-acquisition bottleneck which has apparently brought research in supervised WSD as an explicit application to a plateau. The type of research reported in this thesis, which does not require manually annotated data, holds promise of a lot of new and interesting things, and our work is one of the first steps, despite being a small one, in this direction. The complete system is built and tested on standard benchmarks, and is comparable with work done on graph-based word sense disambiguation as well as lexical chains. The evaluation indicates that the right combination of the above mentioned metrics can be used to develop an unsupervised disambiguation engine as powerful as the state-of-the-art in WSD.
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Keyword:
Computational linguistics; disambiguation; Discourse analysis; graph centrality algorithms; measures of semantic similarity; Semantics -- Data processing; Word sense
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URL: http://digital.library.unt.edu/ark:/67531/metadc9736/
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