Automatic Term Categorization by Extracting Knowledge from the Web

This paper addresses the problem of categorizing terms or lexical entities into a predefined set of semantic domains exploiting the knowledge available on-line in the Web. The proposed system can be effectively used for the automatic expansion of thesauri, limiting the human effort to the preparation of a small training set of tagged entities. The classification of terms is performed by modeling the contexts in which terms from the same class usually appear. The Web is exploited as a significant repository of contexts that are extracted by querying one or more search engines. In particular, it is shown how the required knowledge can be obtained directly from the snippets returned by the search engines without the overhead of document downloads. Since the Web is continuously updated "World Wide", this approach allows us to face the problem of open-domain term categorization handling both the geographical and temporal variability of term semantics. The performances attained by ...

• Type: Conference
• Year: 2006
• Where: ECAI
• Authors: Leonardo Rigutini, Ernesto Di Iorio, Marco Ernandes, Marco Maggini
• Keywords: Artificial Intelligence, ECAI 2006, Knowledge Available On-line, Search Engine, Small Training Set