Indexing with WordNet synsets can improve Text Retrieval

TL;DR

Using WordNet synsets for indexing in text retrieval significantly improves performance over traditional word form indexing, especially with manual disambiguation, but automatic disambiguation errors can impact results.

Contribution

This paper demonstrates that synset-based indexing enhances text retrieval effectiveness and explores the effects of disambiguation accuracy on performance.

Findings

Up to 29% improvement with synset indexing

Manual disambiguation yields better results

Automatic disambiguation errors affect retrieval quality

Abstract

The classical, vector space model for text retrieval is shown to give better results (up to 29% better in our experiments) if WordNet synsets are chosen as the indexing space, instead of word forms. This result is obtained for a manually disambiguated test collection (of queries and documents) derived from the Semcor semantic concordance. The sensitivity of retrieval performance to (automatic) disambiguation errors when indexing documents is also measured. Finally, it is observed that if queries are not disambiguated, indexing by synsets performs (at best) only as good as standard word indexing.