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In this paper, we present a unified model for the automatic induction of word senses from text, and the subsequent disambiguation of particular word instances using the automatically extracted sense inventory. The induction step and the disambiguation step are based on the same principle: words and contexts are mapped to a limited number of topical dimensions in a latent semantic word space. The intuition is that a particular sense is associated with a particular topic, so that different senses can be discriminated through their association with particular topical dimensions; in a similar vein, a particular instance of a word. | Latent Semantic Word Sense Induction and Disambiguation Tim Van de Cruys RCEAL University of Cambridge United Kingdom tv234@cam.ac.uk Marianna Apidianaki Alpage INRIA Univ Paris Diderot Sorbonne Paris Cite UMRI-001 75013 Paris France marianna.apidianaki@inria.fr Abstract In this paper we present a unified model for the automatic induction of word senses from text and the subsequent disambiguation of particular word instances using the automatically extracted sense inventory. The induction step and the disambiguation step are based on the same principle words and contexts are mapped to a limited number of topical dimensions in a latent semantic word space. The intuition is that a particular sense is associated with a particular topic so that different senses can be discriminated through their association with particular topical dimensions in a similar vein a particular instance of a word can be disambiguated by determining its most important topical dimensions. The model is evaluated on the semeval-2010 word sense induction and disambiguation task on which it reaches state-of-the-art results. 1 Introduction Word sense induction WSI is the task of automatically identifying the senses of words in texts without the need for handcrafted resources or manually annotated data. The manual construction of a sense inventory is a tedious and time-consuming job and the result is highly dependent on the annotators and the domain at hand. By applying an automatic procedure we are able to only extract the senses that are objectively present in a particular corpus and it allows for the sense inventory to be straightforwardly adapted to a new domain. Word sense disambiguation WSD on the other hand is the closely related task of assigning a sense 1476 label to a particular instance of a word in context using an existing sense inventory. The bulk of WSD algorithms up till now use pre-defined sense inventories such as WordNet that often contain finegrained sense distinctions which .
