Information Retrieval
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Overview of patent retrieval task at NTCIR-3
PATENT '03 Proceedings of the ACL-2003 workshop on Patent corpus processing - Volume 20
Patent document categorization based on semantic structural information
Information Processing and Management: an International Journal
Visualization of patent analysis for emerging technology
Expert Systems with Applications: An International Journal
Automatic discovery of technology trends from patent text
Proceedings of the 2009 ACM symposium on Applied Computing
Automatic query generation for patent search
Proceedings of the 18th ACM conference on Information and knowledge management
Topic tracking based on keywords dependency profile
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
CLEF-IP 2009: retrieval experiments in the intellectual property domain
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
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Prior art search is one of the most common forms of patent search, whose goal is to find patent documents that constitute prior art for a given patent being examined. Current patent search systems are mostly keyword-based, and due to the unique characteristics of patents and their usage, such as embedded structure and the length of patent documents, there are rooms for further improvements. In this paper, we propose a new query formulation method by using keyword dependency relations and semantic tags, which have not been used for prior art search. The key idea of this paper is to make use of patent structure, linguistic clues and use word relations to identify important terms. Moreover, to formulate better queries we attempt to identify what technology area a patent belongs to and what problems/solutions it addresses. Based on our experiments where IPC codes are used for relevance judgments, we show that keyword dependency relation approach achieved 13˜18% improvement in MAP over the traditional tf-idf based term weighting method when a single field is used for query formulation. Furthermore, we obtain 42˜46% improvement in MAP when additional terms are used through pattern-based semantic tagging.