Word sense disambiguation features for taxonomy extraction

Authors

  • Daniil Alexeyevsky National Research University Higher School of Economics, Russia

DOI:

https://doi.org/10.13053/cys-22-3-2967

Keywords:

word sense disambiguation, taxonomy extraction, vector semantics

Abstract

Many NLP tasks, such as fact extraction, coreference reso-lution and alike, rely on existing lexical taxonomies or ontologies. Oneof possible ways to create a lexical taxonomy is to extract taxonomic re-lations from monolingual dictionary or encyclopedia: a semi-formalizedresource designed to contain many such relations. Word-sense disam-biguation (WSD) is a mandatory tool in such approaches. Quality ofextracted taxonomy greatly depends on WSD results. Most WSD approaches can be formulated as machine learning task. For this sake feature representation ranges from collocation vectors as in Leskalgorithm or neural network features in word2vec to highly specializedvector sense representation models such as AdaGram. In this paper weapply several WSD algorithms to dictionary dentions. Our main focusis on inuence of dierent approaches to extract WSD features fromdictionary denitions on WSD performance.

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Published

2018-09-25

Issue

Vol. 22 No. 3 (2018): Thematic issue on Natural Language Processing (Guest Editor: A. Gelbukh)

Section

Articles of the Thematic Issue

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