We present a novel method for clustering Web search results based on Word Sense Induction. First, we acquire the meanings of a query by means of a graph-based clustering algorithm that calculates the maximum spanning tree of the co-occurrence graph of the query. Then we cluster the search results based on their semantic similarity to the induced word senses. We show that our approach improves classical search result clustering methods in terms of both clustering quality and degree of diversification. © 2011 Springer-Verlag Berlin Heidelberg.
Clustering web search results with maximum spanning trees / DI MARCO, Antonio; Navigli, Roberto. - STAMPA. - 6934 LNAI:(2011), pp. 201-212. (Intervento presentato al convegno 12th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2011 tenutosi a Palermo nel 15 September 2011 through 17 September 2011) [10.1007/978-3-642-23954-0_20].
Clustering web search results with maximum spanning trees
DI MARCO, ANTONIO;NAVIGLI, ROBERTO
2011
Abstract
We present a novel method for clustering Web search results based on Word Sense Induction. First, we acquire the meanings of a query by means of a graph-based clustering algorithm that calculates the maximum spanning tree of the co-occurrence graph of the query. Then we cluster the search results based on their semantic similarity to the induced word senses. We show that our approach improves classical search result clustering methods in terms of both clustering quality and degree of diversification. © 2011 Springer-Verlag Berlin Heidelberg.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
