Word Sense Disambiguation Features for Taxonomy Extraction

Many NLP tasks, such as fact extraction, coreference resolution etc, rely on existing lexical taxonomies or ontologies. One of the possible approaches to create a lexical taxonomy is to extract taxonomic relations from a monolingual dictionary or encyclopedia: a semi-formalized resource designed to contain many relations of this kind. Word-sense disambiguation (WSD) is a mandatory tool for such approaches. The quality of the extracted taxonomy greatly depends on WSD results. Most WSD approaches can be posed as machine learning tasks. For this sake feature representation ranges from collocation vectors as in Lesk algorithm or neural network features in Word2Vec to highly specialized word sense representation models such as AdaGram. In this work we apply several WSD algorithms to dictionary definitions. Our main focus is the influence of different approaches to extract WSD features from dictionary definitions on WSD accuracy.