Relevance Feedback Retrieval of Time Series Data

In this paper we examine the question of query parsing for World Wide Web queries and present a novel method for phrase recognition and expansion. Given a training corpus of approximately 16 million Web queries and a handwritten context-free grammar, the EM algorithm is used to estimate the parameters of a probabilistic context-free grammar (PCFG) with a system developed by Carroll [5]. We use the PCFG to compute the most probable parse for a user query, reflecting linguistic structure and word usage of the domain being parsed. The optimal syntactic parse for a user query thus obtained is employed for phrase recognition and expansion. Phrase recognition is used to increase retrieval precision; phrase expansion is applied to make the best use possible of very short Web queries.