BoostCLIR: JP-EN Relevance Marked Patent Corpus (doi:10.11588/data/10001)

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BoostCLIR: JP-EN Relevance Marked Patent Corpus

doi:10.11588/data/10001

heiDATA

2014-06-16

1

Sokolov, Artem; Jehl Laura; Hieber Felix; Ruppert, Eugen; Riezler, Stefan, 2014, "BoostCLIR: JP-EN Relevance Marked Patent Corpus", https://doi.org/10.11588/data/10001, heiDATA, V1

BoostCLIR: JP-EN Relevance Marked Patent Corpus

doi:10.11588/data/10001

Sokolov, Artem (Department of Computational Linguistics)

Jehl Laura (Department of Computational Linguistics)

Hieber Felix (Department of Computational Linguistics)

Ruppert, Eugen (Department of Computational Linguistics)

Riezler, Stefan (Department of Computational Linguistics)

Jehl, Laura

Sokolov, Artem

Ruppert, Eugen

2013

heiDATA

Prof. Dr. Stefan Riezler

2014-05-21

https://doi.org/10.11588/data/10001

Computer and Information Science

BoostCLIR is a bilingual (Japanese-English) corpus of patent abstracts, extracted from the <a href='http://www.ifs.tuwien.ac.at/imp/marec.shtml'>MAREC</a> patent data, and the data from the <a href='http://research.nii.ac.jp/ntcir/data/data-en.html'>NTCIR PatentMT workshop</a> collections, accompanied with relevance judgements for the task of patent prior-art search. <br /><br /> <strong>Important:</strong> The English side of the corpus contains patent IDs as well as the text of the abstracts. The Japanese side only contains patent IDs because of NTCIR copyright restrictions. The Jap anese patent abstracts can be extracted from full text Japanese patent documents, which are available from the organizers of the NTCIR workshop. <br /><br /> The corpus contains training, development and testing subsets sampled from non-intersecting time periods. <br /><br /> Relevance judgement for patent retrieval are constructed from patent citations by assigning three integer levels to three categories of relationships, with highest relevance (3) for family patents, lower relevance for patents cited in search reports by patent examiners (2), and lowest relevance level (1) for applicants’ citations. <br /><br /> For a detailed descrip tion of the corpus construction process, please see the above publication.

textual data

http://www.cl.uni-heidelberg.de/statnlpgroup/boostclir/

1.4M documents, 100K queries

If you use the corpus in your work, please cite: Artem Sokolov, Laura Jehl, Felix Hieber, Stefan Riezler. "Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings". In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, USA, 2013

<ul> <li>MAREC dataset: <a href='http://www.ifs.tuwien.ac.at/imp/marec.shtml'>http://www.ifs.tuwien.ac.at/imp/marec.shtml</a></li> <li>NTCIR collections: <a href='http://research.nii.ac.jp/ntcir/data/data-en.html'>http://research.nii.ac.jp/ntcir/data/data-en.html</a></li> </ul>

Artem Sokolov, Laura Jehl, Felix Hieber, Stefan Riezler. "Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings". In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, USA, 2013

Artem Sokolov, Laura Jehl, Felix Hieber, Stefan Riezler. "Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings". In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, USA, 2013

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