acl acl2011 acl2011-182 acl2011-182-reference knowledge-graph by maker-knowledge-mining
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Author: Michael Bendersky ; W. Bruce Croft ; David A. Smith
Abstract: W. Bruce Croft Dept. of Computer Science University of Massachusetts Amherst, MA cro ft @ c s .uma s s .edu David A. Smith Dept. of Computer Science University of Massachusetts Amherst, MA dasmith@ c s .umas s .edu articles or web pages). As previous research shows, these differences severely limit the applicability of Marking up search queries with linguistic annotations such as part-of-speech tags, capitalization, and segmentation, is an impor- tant part of query processing and understanding in information retrieval systems. Due to their brevity and idiosyncratic structure, search queries pose a challenge to existing NLP tools. To address this challenge, we propose a probabilistic approach for performing joint query annotation. First, we derive a robust set of unsupervised independent annotations, using queries and pseudo-relevance feedback. Then, we stack additional classifiers on the independent annotations, and exploit the dependencies between them to further improve the accuracy, even with a very limited amount of available training data. We evaluate our method using a range of queries extracted from a web search log. Experimental results verify the effectiveness of our approach for both short keyword queries, and verbose natural language queries.
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