Understanding the Semantic Structure of Noun Phrase Queries

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1337–1345, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Understanding the Semantic Structure of Noun Phrase Queries Xiao Li Microsoft Research One Microsoft Way Redmond, WA 98052 USA xiaol@microsoft.com Abstract Determining the semantic intent of web queries not only involves identifying their semantic class, which is a primary focus of previous works, but also understanding their semantic structure. In this work, we formally define the semantic structure of noun phrase queries as comprised of intent heads and intent modifiers. We present methods that automatically identify these constituents as well as their semantic roles based on Markov and semi-Markov con- ditional random fields. We show that the use of semantic features and syntactic fea- tures significantly contribute to improving the understanding performance. 1 Introduction Web queries can be considered as implicit ques- tions or commands, in that they are performed ei- ther to find information on the web or to initiate interaction with web services. Web users, how- ever, rarely express their intent in full language. For example, to find out “what are the movies of 2010 in which johnny depp stars”, a user may sim- ply query “johnny depp movies 2010”. Today’s search engines, generally speaking, are based on matching such keywords against web documents and ranking relevant results using sophisticated features and algorithms. As search engine technologies evolve, it is in- creasingly believed that search will be shifting away from “ten blue links” toward understanding intent and serving objects. This trend has been largely driven by an increasing amount of struc- tured and semi-structured data made available to search engines, such as relational databases and semantically annotated web documents. Search- ing over such data sources, in many cases, can offer more relevant and essential results com- pared with merely returning web pages that con- tain query keywords. Table 1 shows a simplified view of a structured data source, where each row represents a movie object. Consider the query “johnny depp movies 2010”. It is possible to re- trieve a set of movie objects from Table 1 that satisfy the constraints Year = 2010 and Cast ∋ Johnny Depp. This would deliver direct answers to the query rather than having the user sort through list of keyword results. In no small part, the success of such an ap- proach relies on robust understanding of query in- tent. Most previous works in this area focus on query intent classification (Shen et al., 2006; Li et al., 2008b; Arguello et al., 2009). Indeed, the intent class information is crucial in determining if a query can be answered by any structured data sources and, if so, by which one. In this work, we go one step further and study the semantic struc- ture of a query, i.e., individual constituents of a query and their semantic roles. In particular, we focus on noun phrase queries. A key contribution of this work is that we formally define query se- mantic …

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