Title

Improving Supervised Sense Disambiguation With Web-Scale Selectors

Keywords

Lexical semantics; Semi-supervised learning; Word sense disambiguation

Abstract

This paper introduces a method to improve supervised word sense disambiguation performance by including a new class of features which leverage contextual information from large unannotated corpora. This new feature class, selectors, contains words that appear in other corpora with the same local context as a given lexical instance. We show that support vector sense classifiers trained with selectors achieve higher accuracy than those trained only with standard features, producing error reductions of 15.4% and 6.9% on standard coarse-grained and fine-grained disambiguation tasks respectively. Furthermore, we find an error reduction of 9.3% when including selectors for the classification step of named-entity recognition over a representative sample of OntoNotes. These significant improvements come free of any human annotation cost, only requiring unlabeled Web-Scale corpora. © 2012 The COLING.

Publication Date

12-1-2012

Publication Title

24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers

Number of Pages

2423-2440

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84876794877 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84876794877

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