Previous research has conflicting conclusions on whether word sense disambiguation (WSD) systems can improve information retrieval (IR) performance.

Together with the senses predicted for words in documents, we propose a novel approach to incorporate word senses into the language modeling approach to IR and also exploit the integration of synonym relations.

Our experimental results on standard TRE C collections show that using the word senses tagged by a supervised WSD system, we obtain significant improvements over a state-of-the-art IR system.

Word sense disambiguation (WSD) is the task of identifying the correct meaning of a word in context.

Some of the early research showed a drop in retrieval performance by using word senses (Krovetz and Croft, 1992; Voorhees, 1993).

Some other experiments observed improvements by integrating word senses in IR systems (Schutze and Pedersen, 1995; Gonzalo et al., 1998; Stokoe et al., 2003; Kim et al., 2004).

However, it is hard to judge the effect of word senses because of the overall poor performances of their baseline method and their system.

4.1 Word sense disambiguation system

Reisinger and Mooney (2010b) introduced a multi-prototype VSM where word sense discrimination is first applied by clustering contexts, and then prototypes are built using the contexts of the sense-labeled words.

We present a way to use our learned single-prototype embeddings to represent each context window, which can then be used by clustering to perform word sense discrimination (Schutze, 1998).

The multi-prototype approach has been widely studied in models of categorization in psychology (Rosseel, 2002; Griffiths et al., 2009), while Schutze (1998) used clustering of contexts to perform word sense discrimination.

th is similar to that of Yarowsky (1995) but is better specified and omits word sense disambiguation optimizations.

The tasks of Eisner and Karakos (2005) are word sense disambiguation on several English words which have two senses corresponding to two different words in French.

There is no difference on the word sense data sets.