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Human and machine judgements for Russian semantic relatedness
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In: Commun. Comput. Info. Sci. ; Communications in Computer and Information Science (2017)
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Thésaurus distributionnels pour la recherche d'information et vice-versa
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In: Conférence en Recherche d’Information et Applications ; https://hal.archives-ouvertes.fr/hal-01226532 ; Conférence en Recherche d’Information et Applications, Mar 2015, Paris, France (2015)
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Thésaurus distributionnels pour la recherche d'information et vice-versa
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In: ISSN: 1279-5127 ; EISSN: 1963-1014 ; Document Numérique ; https://hal.archives-ouvertes.fr/hal-01226551 ; Document Numérique, Lavoisier, 2015, 18 (2-3), ⟨10.3166/DN.18.2-3.101-121⟩ (2015)
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Automatic Supervised Thesauri Construction with Roget’s Thesaurus
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Using Distributional Similarity for Lexical Expansion in Knowledge-based Word Sense Disambiguation
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In: http://aclweb.org/anthology/C/C12/C12-1109.pdf (2012)
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Automatic Supervised Thesauri Construction with Roget’s Thesaurus ...
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Automatic Supervised Thesauri Construction with Roget’s Thesaurus
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Measuring Semantic Distance using Distributional Profiles of Concepts
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Abstract:
Semantic distance is a measure of how close or distant in meaning two units of language are. A large number of important natural language problems, including machine translation and word sense disambiguation, can be viewed as semantic distance problems. The two dominant approaches to estimating semantic distance are the WordNet-based semantic measures and the corpus-based distributional measures. In this thesis, I compare them, both qualitatively and quantitatively, and identify the limitations of each. This thesis argues that estimating semantic distance is essentially a property of concepts (rather than words) and that two concepts are semantically close if they occur in similar contexts. Instead of identifying the co-occurrence (distributional) profiles of words (distributional hypothesis), I argue that distributional profiles of concepts (DPCs) can be used to infer the semantic properties of concepts and indeed to estimate semantic distance more accurately. I propose a new hybrid approach to calculating semantic distance that combines corpus statistics and a published thesaurus (Macquarie Thesaurus). The algorithm determines estimates of the DPCs using the categories in the thesaurus as very coarse concepts and, notably, without requiring any sense-annotated data. Even though the use of only about 1000 concepts to represent the vocabulary of a language seems drastic, I show that the method achieves results better than the state-of-the-art in a number of natural language tasks. I show how cross-lingual DPCs can be created by combining text in one language with a thesaurus from another. Using these cross-lingual DPCs, we can solve problems in one, possibly resource-poor, language using a knowledge source from another, possibly resource-rich, language. I show that the approach is also useful in tasks that inherently involve two or more languages, such as machine translation and multilingual text summarization. The proposed approach is computationally inexpensive, it can estimate both semantic relatedness and semantic similarity, and it can be applied to all parts of speech. Extensive experiments on ranking word pairs as per semantic distance, real-word spelling correction, solving Reader's Digest word choice problems, determining word sense dominance, word sense disambiguation, and word translation show that the new approach is markedly superior to previous ones.
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Keyword:
0800; 0984; Computational Linguistics; corpus-based techniques; cross-lingual techniques; distributional profiles of concepts; distributional similarity; Lexical semantics; Natural Language Processing; semantic distance; semantic relatedness; semantic similarity; thesaurus; word concept co-occurrence matrix; word sense disambiguation; word sense dominance; word senses; wordnet
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URL: http://hdl.handle.net/1807/11238
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NATURAL LANGUAGE PROCESSING AND SOCIAL NETWORK ANALYSIS STUDYING SPECIAL TEXT RUSSIAN CORPORA BY THE LEXICO-SYNTACTIC MODELS
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In: http://www.foibg.com/ibs_isc/ibs-27/ibs-27-p12.pdf
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Interpolated PLSI for Learning Plausible Verb Arguments ∗ a, b
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In: http://www.aclweb.org/anthology-new/Y/Y09/Y09-2021.pdf
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| 12 |
Measuring Semantic Distance using Distributional Profiles of Concepts
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