Word sense disambiguation features for taxonomy extraction

Abstract

Many NLP tasks, such as fact extraction, coreference reso- lution and alike, rely on existing lexical taxonomies or ontologies. One of possible ways to create a lexical taxonomy is to extract taxonomic re- lations from monolingual dictionary or encyclopedia: a semi-formalized resource designed to contain many such relations. Word-sense disam- biguation (WSD) is a mandatory tool in such approaches. Quality of extracted 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 Lesk algorithm or neural network features in word2vec to highly specialized vector sense representation models such as AdaGram. In this paper we apply several WSD algorithms to dictionary dentions. Our main focus is on inuence of dierent approaches to extract WSD features from dictionary denitions on WSD performance.