Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Retrievability: an evaluation measure for higher order information access tasks
Proceedings of the 17th ACM conference on Information and knowledge management
Positional language models for information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A signal-to-noise approach to score normalization
Proceedings of the 18th ACM conference on Information and knowledge management
Automatic query generation for patent search
Proceedings of the 18th ACM conference on Information and knowledge management
Positional relevance model for pseudo-relevance feedback
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
PRES: a score metric for evaluating recall-oriented information retrieval applications
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
PATATRAS: retrieval model combination and regression models for prior art search
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Current Challenges in Patent Information Retrieval
Current Challenges in Patent Information Retrieval
Patent query reduction using pseudo relevance feedback
Proceedings of the 20th ACM international conference on Information and knowledge management
A study on query expansion methods for patent retrieval
Proceedings of the 4th workshop on Patent information retrieval
Improving retrievability of patents in prior-art search
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Promoting ranking diversity for biomedical information retrieval using wikipedia
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Reliability prediction of webpages in the medical domain
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Effective query generation and postprocessing strategies for prior art patent search
Journal of the American Society for Information Science and Technology
Automatic refinement of patent queries using concept importance predictors
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Aggregation Methods for Proximity-Based Opinion Retrieval
ACM Transactions on Information Systems (TOIS)
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Patent prior art search is a task in patent retrieval where the goal is to rank documents which describe prior art work related to a patent application. One of the main properties of patent retrieval is that the query topic is a full patent application and does not represent a focused information need. This query by document nature of patent retrieval introduces new challenges and requires new investigations specific to this problem. Researchers have addressed this problem by considering different information resources for query reduction and query disambiguation. However, previous work has not fully studied the effect of using proximity information and exploiting domain specific resources for performing query disambiguation. In this paper, we first reduce the query document by taking the first claim of the document itself. We then build a query-specific patent lexicon based on definitions of the International Patent Classification (IPC). We study how to expand queries by selecting expansion terms from the lexicon that are focused on the query topic. The key problem is how to capture whether an expansion term is focused on the query topic or not. We address this problem by exploiting proximity information. We assign high weights to expansion terms appearing closer to query terms based on the intuition that terms closer to query terms are more likely to be related to the query topic. Experimental results on two patent retrieval datasets show that the proposed method is effective and robust for query expansion, significantly outperforming the standard pseudo relevance feedback (PRF) and existing baselines in patent retrieval.