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Joint Annotation of Search Queries
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BibTeX
@MISC{Bendersky_jointannotation,
author = {Michael Bendersky and W. Bruce Croft and David A. Smith},
title = {Joint Annotation of Search Queries},
year = {}
}
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Abstract
Marking up search queries with linguistic annotations such as part-of-speech tags, capitalization, and segmentation, is an important 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. 1
Keyphrases
search query joint annotation joint query annotation web search log verbose natural language query information retrieval system part-of-speech tag additional classifier available training data independent annotation probabilistic approach robust set unsupervised independent annotation short keyword query idiosyncratic structure pseudo-relevance feedback limited amount important part linguistic annotation nlp tool query processing experimental result
