Although term extraction has been researched for more than 20 years, only a few studies focus on under-resourced languages. Moreover, bilingual term mapping from comparable corpora for these languages has attracted researchers only recently. This paper presents methods for term extraction, term tagging in documents, and bilingual term mapping from comparable corpora for four under-resourced languages: Croatian, Latvian, Lithuanian, and Romanian. Methods described in this paper are language independent as long as language specific parameter data is provided by the user and the user has access to a part of speech or a morpho-syntactic tagger.
In this project, we provide our implementations of CNN [Zeng et al., 2014] and PCNN [Zeng et al.,2015] and their extended version with sentence-level attention scheme [Lin et al., 2016] .
NYT10 is originally released by the paper "Sebastian Riedel, Limin Yao, and Andrew McCallum. Modeling relations and their mentions without labeled text."
Relation extraction on an open-domain knowledge base
Accompanying repository for our EMNLP 2017 paper. It contains the code to replicate the experiments and the pre-trained models for sentence-level relation extraction.
Anything To Triples (any23) is a library, a web service and a command line tool that extracts structured data in RDF format from a variety of Web documents.
To help researchers investigate relation extraction, we’re releasing a human-judged dataset of two relations about public figures on Wikipedia: nearly 10,000 examples of “place of birth”, and over 40,000 examples of “attended or graduated from an institution”. Each of these was judged by at least 5 raters, and can be used to train or evaluate relation extraction systems. We also plan to release more relations of new types in the coming months.
To help researchers investigate relation extraction, we’re releasing a human-judged dataset of two relations about public figures on Wikipedia: nearly 10,000 examples of “place of birth”, and over 40,000 examples of “attended or graduated from an institution”. Each of these was judged by at least 5 raters, and can be used to train or evaluate relation extraction systems. We also plan to release more relations of new types in the coming months.
Y. Ohsawa, N. Benson, and M. Yachida. ADL '98: Proceedings of the Advances in Digital Libraries Conference, page 12. Washington, DC, USA, IEEE Computer Society, (1998)
Y. Matsuo, and M. Ishizuka. Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference, page 392-396. AAAI Press, (2003)
J. Chang, J. Boyd-Graber, and D. Blei. KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, page 169--178. New York, NY, USA, ACM, (2009)
P. Kluegl, M. Atzmueller, and F. Puppe. Proceedings of the Biennial GSCL Conference 2009, 2nd UIMA@GSCL Workshop, page 233-240. Gunter Narr Verlag, (2009)
T. Rattenbury, N. Good, and M. Naaman. SIGIR '07: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, page 103--110. New York, NY, USA, ACM Press, (2007)
X. Wan, and J. Xiao. Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), page 969--976. Manchester, UK, Coling 2008 Organizing Committee, (August 2008)