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By Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan and Gadi Wolfman


Keyword-based search engines are in widespread use today as a popular means for Web-based information retrieval. Although such systems seem deceptively simple, a considerable amount of skill is required in order to satisfy non-trivial information needs. This paper presents a new conceptual paradigm for performing search in context that largely automates the search process, providing even non-professional users with highly relevant results. This paradigm is implemented in practice in the IntelliZap system, where search is initiated from a text query marked by the user in a document she views, and is guided by the text surrounding the marked query in that document (“the context”). The context-driven information retrieval process involves semantic keyword extraction and clustering to automatically generate new, augmented queries. The latter are submitted to a host of general and domain-specific search engines. Search results are then semantically reranked, again, using context. Experimental results testify that using context to guide search effectively offers even inexperienced users an advanced search tool on the Web

Topics: Context, Semantic Processing, Invisible Web, Statistical Natural Language Processing
Year: 2011
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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