Precomputing Search Features for Fast and Accurate Query Classification
published: Feb. 22, 2010, recorded: February 2010, views: 3323
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Query intent classification is crucial for web search and advertising. It is known to be challenging because web queries contain less than three words on average, and so provide little signal to base classification decisions on. At the same time, the vocabulary used in search queries is vast: thus, classifiers based on word-occurrence have to deal with a very sparse feature space, and often require large amounts of training data. Prior efforts to address the issue of feature sparseness augmented the feature space using features computed from the results obtained by issuing the query to be classified against a web search engine. However, these approaches induce high latency, making them unacceptable in practice. In this paper, we propose a new class of features that realizes the benefit of search-based features without high latency. These leverage co-occurrence between the query keywords and tags applied to documents in search results, resulting in a significant boost to web query classification accuracy. By precomputing the tag incidence for a suitably chosen set of keyword-combinations, we are able to generate the features online with low latency and memory requirements. We evaluate the accuracy of our approach using a large corpus of real web queries in the context of commercial search.
Download slides: wsdm2010_konig_psf_01.pdf (2.7 MB)
Download slides: wsdm2010_konig_psf_01.ppt (2.7 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !