Joint Annotation of Search Queries
Michael Bendersky, W. Bruce Croft, David A. Smith · 2011
Marking up search queries with linguistic an-notations such as part-of-speech tags, cap-italization, and segmentation, is an impor-tant part of query processing and understand-ing 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 perform-ing joint query annotation. First, we derive a robust set of unsupervised independent an-notations, using queries and pseudo-relevance feedback. Then, we stack additional classi-fiers on the independent annotations, and ex-ploit the dependencies between them to fur-ther 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. Experimen-tal results verify the effectiveness of our ap-proach for both short keyword queries, and verbose natural language queries. 1