Term clustering of syntactic phrases
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Improving two-stage ad-hoc retrieval for short queries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Phase-based information retrieval
Information Processing and Management: an International Journal
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
On document relevance and lexical cohesion between query terms
Information Processing and Management: an International Journal
Query expansion with terms selected using lexical cohesion analysis of documents
Information Processing and Management: an International Journal
Clustering short texts using wikipedia
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Improving weak ad-hoc queries using wikipedia asexternal corpus
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A cluster-based resampling method for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Adapting information retrieval to query contexts
Information Processing and Management: an International Journal
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Query Expansion Using External Evidence
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Concept and Context in Legal Information Retrieval
Proceedings of the 2008 conference on Legal Knowledge and Information Systems: JURIX 2008: The Twenty-First Annual Conference
Query dependent pseudo-relevance feedback based on wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Transforming patents into prior-art queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Adaptive relevance feedback in information retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Phrase-based document categorization revisited
Proceedings of the 2nd international workshop on Patent information retrieval
Search system requirements of patent analysts
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Improved latent concept expansion using hierarchical markov random fields
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Relevance Feedback methods generally suffer from topic drift caused by words ambiguity and synonymous uses of words. As a way to alleviate the inherent problem, we propose a novel query phrase expansion approach utilizing semantic annotations in Wikipedia pages, trying to enrich queries with context disambiguating phrases. Focusing on the patent domain, especially on patent search where patents are classified into a hierarchy of categories, we attempt to understand the roles of phrases and words in query expansion in determining the relevance of documents and examine their contributions to alleviating the query drift problem. Our approach is compared against Relevance Model, a state-of-the-art, to show its superiority in terms of MAP on all levels of the classification hierarchy.