Lexical Query Paraphrasing for Document Retrieval

Lexical Query Paraphrasing for Document Retrieval � Ingrid Zukerman School of Computer Science and Software Eng. Monash University Clayton, VICTORIA 3800 AUSTRALIA Bhavani Raskutti Telstra Research Laboratories 770 Blackburn Road Clayton, VICTORIA 3168 AUSTRALIA Abstract We describe a mechanism for the generation of lexical paraphrases of queries posed to an Inter- net resource. These paraphrases are generated us- ing WordNet and part-of-speech information to pro- pose synonyms for the content words in the queries. Statistical information, obtained from a corpus, is then used to rank the paraphrases. We evaluated our mechanism using 404 queries whose answers reside in the LA Times subset of the TREC-9 cor- pus. There was a 14% improvement in perfor- mance when paraphrases were used for document 1retrieval. Introduction The vocabulary of users of domain-specific retrieval systems often differs from the vocabulary within a particular resource, leading to retrieval failure. In this research, we address this problem by submit- ting multiple paraphrases of a query to a retrieval system, in the hope that one or more of the posited paraphrases will match a relevant document. We focus on the generation of lexical paraphrases for queries posed to the Internet. These are para- phrases where content words are replaced with syn- onyms. We use WordNet (Miller et al., 1990) and part-of-speech information to propose these syn- onyms, and build candidate paraphrases from com- binations of these synonyms. The resultant para- phrases are then scored using word co-occurrence information obtained from a corpus, and the high- est scoring paraphrases are retained. Our evaluation shows a 14% improvement in retrieval performance as a result of query paraphrasing. In the next section we describe related research. In Section 3, we discuss the resources used by our mechanism. The paraphrase generation and docu- ment retrieval processes are described in Section 4. Section 5 presents sample paraphrases, followed by our evaluation and concluding remarks. � This research was supported in part by Australian Research Council grant DP0209565. 2 Related Research The vocabulary mis-match between user queries and indexed documents is often addressed through query expansion. Two common techniques for query expansion are blind relevance feedback (Buckley et al., 1995; Mitra et al., 1998) and word sense disambiguation (WSD) (Mihalcea and Moldovan, 1999; Lytinen et al., 2000; Sch¨utze and Pedersen, 1995; Lin, 1998). Blind relevance feed- back consists of retrieving a small number of docu- ments using a query given by a user, and then con- structing an expanded query that includes content words that appear frequently in these documents. This expanded query is used to retrieve a new set of documents. WSD often precedes query expansion to avoid retrieving irrelevant information. Mihalcea and Moldovan (1999) and Lytinen et al. (2000) used a machine readable thesaurus, specifically WordNet (Miller et al., 1990), to obtain the sense of a word, while Sch¨utze and Pedersen (1995) and Lin (1998) used automatically constructed thesauri. The improvements in retrieval performance re- ported in (Mitra et al., 1998) are comparable to those reported here (note that these researchers …

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