Semantic is beautiful: clustering and
   diversifying search results with graph-based
              Word Sense Induction

                                  Roberto Navigli

              Department of Computer Science, Sapienza University of Rome
                               navigli@di.uniroma1.it




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 pos-
sibly 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 notion of textual similarity of
search results. As a result, text snippets with no word in common tend to be clus-
tered separately, even if they share the same meaning.
In this talk, 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 in the clustering and diversification of search results.