The patent mining task in the seventh NTCIR workshop
Proceedings of the 1st ACM workshop on Patent information retrieval
Whetting the appetite of scientists: producing summaries tailored to the citation context
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Automatic translation of scholarly terms into patent terms
Proceedings of the 2nd international workshop on Patent information retrieval
Classification of research papers into a patent classification system using two translation models
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
Web Semantics: Science, Services and Agents on the World Wide Web
Automatic extraction and resolution of bibliographical references in patent documents
IRFC'10 Proceedings of the First international Information Retrieval Facility conference on Adbances in Multidisciplinary Retrieval
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The need for academic researchers to retrieve patents and research papers is increasing, because applying for patents is now considered an important research activity. However, retrieving patents using keywords is a laborious task for researchers, because the terms used in patents for the purpose of enlarging the scope of the claims are generally more abstract than those used in research papers. Therefore, we have constructed a framework that facilitates patent retrieval for researchers, and have integrated research papers and patents by analysing the citation relationships between them. We obtained cited research papers in patents using two steps: (1) detection of sentences containing bibliographic information, and (2) extraction of bibliographic information from those sentences. To investigate the effectiveness of our method, we conducted two experiments. In the experiment involving Step 1, we prepared 42,073 sentences, among which a human subject manually identified 1,476 sentences containing citations of papers. For Step 2, we prepared 3,000 sentences, in which the titles, authors, and other bibliographic information were manually identified. We obtained a precision of 91.6%, and a recall of 86.9% in Step 1, and a precision of 86.2% and a recall of 85.1% in Step 2. Finally, we constructed an information retrieval system that provided two methods of retrieving research papers and patents. One method was retrieval by query, and another was from the citation relationships between research papers and patents.