Fine-Grained Class Label Markup of Search Queries

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1200–1209, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Fine-Grained Class Label Markup of Search Queries Joseph Reisinger∗ Department of Computer Sciences The University of Texas at Austin Austin, Texas 78712 joeraii@cs.utexas.edu Marius Pas¸ca Google Inc. 1600 Amphitheatre Parkway Mountain View, California 94043 mars@google.com Abstract We develop a novel approach to the seman- tic analysis of short text segments and demon- strate its utility on a large corpus of Web search queries. Extracting meaning from short text segments is difficult as there is little semantic redundancy between terms; hence methods based on shallow semantic analy- sis may fail to accurately estimate meaning. Furthermore search queries lack explicit syn- tax often used to determine intent in ques- tion answering. In this paper we propose a hybrid model of semantic analysis combin- ing explicit class-label extraction with a la- tent class PCFG. This class-label correlation (CLC) model admits a robust parallel approxi- mation, allowing it to scale to large amounts of query data. We demonstrate its performance in terms of (1) its predicted label accuracy on polysemous queries and (2) its ability to accu- rately chunk queries into base constituents. 1 Introduction Search queries are generally short and rarely contain much explicit syntax, making query understanding a purely semantic endeavor. Furthermore, as in noun- phrase understanding, shallow lexical semantics is often irrelevant or misleading; e.g., the query [trop- ical breeze cleaners] has little to do with island va- cations, nor are desert birds relevant to [1970 road runner], which refers to a car model. This paper introduces class-label correlation (CLC), a novel unsupervised approach to extract- ∗Contributions made during an internship at Google. ing shallow semantic content that combines class- based semantic markup (e.g., road runner is a car model) with a latent variable model for capturing weakly compositional interactions between query constituents. Constituents are tagged with IsA class labels from a large, automatically extracted lexicon, using a probabilistic context free grammar (PCFG). Correlations between the resulting label→term dis- tributions are captured using a set of latent produc- tion rules specified by a hierarchical Dirichlet Pro- cess (Teh et al., 2006) with latent data groupings. Concretely, the IsA tags capture the inventory of potential meanings (e.g., jaguar can be labeled as european car or large cat) and relevant con- stituent spans, while the latent variable model per- forms sense and theme disambiguation (e.g., [jaguar habitat] would lend evidence for the large cat la- bel). In addition to broad sense disambiguation, CLC can distinguish closely related usages, e.g., the use of dell in [dell motherboard replacement] and [dell stock price].1 Furthermore, by employing IsA class labeling as a preliminary step, CLC can account for common non-compositional phrases, such as big ap- ple unlike systems relying purely on lexical seman- tics. Additional examples can be found later, in Fig- ure 5. In addition to improving query understanding, po- tential applications of CLC include: (1) relation ex- traction (Baeza-Yates and …

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