The Linguistic Structure of English Web-Search Queries

The Linguistic Structure of English Web-Search Queries

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by Cory Barr , Rosie Jones , Moira Regelson

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Datum	Value	Source
TITLE	The Linguistic Structure of English Web-Search Queries	SVM HeaderParse 0.2
AUTHOR NAME	Cory Barr	SVM HeaderParse 0.2
AUTHOR AFFIL	Yahoo! Inc.	SVM HeaderParse 0.2
AUTHOR ADDR	701 First Ave; Sunnyvale, CA 94089	SVM HeaderParse 0.2
AUTHOR NAME	Rosie Jones	SVM HeaderParse 0.2
AUTHOR AFFIL	Yahoo! Inc.	SVM HeaderParse 0.2
AUTHOR ADDR	701 First Ave; Sunnyvale, CA 94089	SVM HeaderParse 0.2
AUTHOR NAME	Moira Regelson	SVM HeaderParse 0.2
AUTHOR AFFIL	Perfect Market, Inc.	SVM HeaderParse 0.2
AUTHOR ADDR	Pasadena, CA 91103	SVM HeaderParse 0.2
ABSTRACT	Web-search queries are known to be short, but little else is known about their structure. In this paper we investigate the applicability of part-of-speech tagging to typical Englishlanguage web search-engine queries and the potential value of these tags for improving search results. We begin by identifying a set of part-of-speech tags suitable for search queries and quantifying their occurrence. We find that proper-nouns constitute 40 % of query terms, and proper nouns and nouns together constitute over 70 % of query terms. We also show that the majority of queries are nounphrases, not unstructured collections of terms. We then use a set of queries manually labeled with these tags to train a Brill tagger and evaluate its performance. In addition, we investigate classification of search queries into grammatical classes based on the syntax of part-of-speech tag sequences. We also conduct preliminary investigative experiments into the practical applicability of leveraging query-trained part-of-speech taggers for information-retrieval tasks. In particular, we show that part-of-speech information can be a significant feature in machine-learned searchresult relevance. These experiments also include the potential use of the tagger in selecting words for omission or substitution in query reformulation, actions which can improve recall. We conclude that training a partof-speech tagger on labeled corpora of queries significantly outperforms taggers based on traditional corpora, and leveraging the unique linguistic structure of web-search queries can improve search experience. 1	SVM HeaderParse 0.2
CITATIONS	14 found	ParsCit 1.0