Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction

Abstract

Web search result clustering aims to facilitate information
search on the Web. Rather than presenting the results of a query
as a flat list, these are grouped on the basis of their similarity
and subsequently shown to the user as a list of possibly labeled clusters.
Each cluster is supposed to represent a different meaning
of the input query, thus taking into account the language ambiguity
issue. However, Web clustering methods typically rely on some shallow
notion of textual similarity of search result snippets. As a result,
text snippets with no word in common tend to be clustered separately,
even if they share the same meaning, whereas snippets with words in
common may be grouped together even if they refer to different meanings
of the input query.

In this paper, we present a novel approach to Web search result clustering
based on the automatic discovery of word senses from raw text, a task
referred to as Word Sense Induction (WSI). Key to our approach is to
first acquire the senses (i.e., meanings) of a query and then cluster
the search results based on their semantic similarity to the word senses
induced. Our experiments, conducted on datasets of ambiguous queries,
show that our approach outperforms both Web clustering and search engines.