Fine-Grained Class Label Markup of Search Queries
Joseph Reisinger, MARIUS A. PAŞCA · 2011
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