Genomics & Informatics

  • 제17권2호
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  • Pages.21.1-21.6
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  • 2019
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  • 1598-866X(pISSN)
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  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
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Improving spaCy dependency annotation and PoS tagging web service using independent NER services

  • Colic, Nico (Institute of Computational Linguistics, University of Zurich) ;
  • Rinaldi, Fabio (Institute of Computational Linguistics, University of Zurich)
  • 투고 : 2019.02.23
  • 심사 : 2019.05.31
  • 발행 : 2019.06.30
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초록

Dependency parsing is often used as a component in many text analysis pipelines. However, performance, especially in specialized domains, suffers from the presence of complex terminology. Our hypothesis is that including named entity annotations can improve the speed and quality of dependency parses. As part of BLAH5, we built a web service delivering improved dependency parses by taking into account named entity annotations obtained by third party services. Our evaluation shows improved results and better speed.

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참고문헌

  1. Nguyen TH, Grishman R. Relation extraction: perspective from convolutional neural networks. In: 2015 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT 2015), 2015 May 31-Jun 5, Denver, CO, USA. Stroudsburg: Association for Computational Linguistics, 2015. pp. 39-48.
  2. Colic N. Dependency parsing for relation extraction in biomedical literature [thesis]. Zurich: University of Zurich, 2016.
  3. Levy O, Goldberg Y. Dependency-based word embeddings. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers) (Toutanova K, Wu H, eds.), 2014 Jun 23-25, Baltimore, MD, USA. Stroudsburg: Association for Computational Linguistics, 2014. pp. 302-308.
  4. Zeman D, Hajic J, Popel M, Potthast M, Straka M, Ginter F, et al. CoNLL 2018 shared task: multilingual parsing from raw text to universal dependencies. In: Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman D, Hajic J, Popel M, Straka M, Nivre J, Ginter F, et al., eds.), 2018 Oct 31-Nov 1, Brussels, Belgium. Stroudsburg: Association for Computational Linguistics, 2018. pp. 1-21.
  5. Rinaldi F, Dowdall J, Hess M, Kaljurand K, Koitand M, Kahusk N, et al. Terminology as knowledge in answer extraction. In: Proceedings of the 6th International Conference on Terminology and Knowledge Engineering (TKE 2002) (Melby A, ed.), 2002 Aug 28-30, Nancy, France. Le Chesnay: INRIA, 2002. pp. 107-113.
  6. Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C. Neural architectures for named entity recognition. Ithaca: arXiv, Cornell University, 2016. Accessed 2019 Apr 2. Available from: https://arxiv.org/abs/1603.01360.
  7. Honnibal M, Johnson M. An improved non-monotonic transition system for dependency parsing. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015 Sep 17-21, Lisbon, Portugal. Stroudsburg: Association for Computational Linguistics, 2015. pp. 1373-1378.
  8. Furrer L, Jancso A, Colic N, Rinaldi F. OGER++: hybrid multi-type entity recognition. J Cheminform 2019;11:7. https://doi.org/10.1186/s13321-018-0326-3
  9. Pletscher-Frankild S, Jensen LJ. Design, implementation, and operation of a rapid, robust named entity recognition web service. J Cheminform 2019;11:19. https://doi.org/10.1186/s13321-019-0344-9
  10. Leaman R, Lu Z. TaggerOne: joint named entity recognition and normalization with semi-Markov Models. Bioinformatics 2016;32:2839-2846. https://doi.org/10.1093/bioinformatics/btw343
  11. Choi JD, Tetreault J, Stent A. It depends: dependency parser comparison using a web-based evaluation tool. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Zong C, Strube M, eds.), 2015 Jul 26-31, Beijing, China. Stroudsburg: Association for Computational Linguistics, 2015. pp. 387-396.
  12. Oepen S, Kuhlmann M, Miyao Y, Zeman D, Cinkova S, Flickinger D, et al. SemEval 2015 task 18: broad-coverage semantic dependency parsing. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) (Nakov P, Zesch T, Cer D, Jurgens D, eds.), 2015 Jun, Denver, CO, USA. Stroudsburg: Association for Computational Linguistics, 2015. pp. 915-926.
  13. Dozat T, Manning CD. Deep biaffine attention for neural dependency parsing. Ithaca: arXiv, Cornell University, 2016. Accessed 2019 Apr 2. Available from: https://arxiv.org/abs/1611.01734.
  14. Kiperwasser E, Goldberg Y. Simple and accurate dependency parsing using bidirectional LSTM feature representations. Trans Assoc Comput Linguist 2016;4:313-327. https://doi.org/10.1162/tacl_a_00101
  15. Yang H, Zhuang T, Zong C. Domain adaptation for syntactic and semantic dependency parsing using deep belief networks. Trans Assoc Comput Linguist 2015;3:271-282. https://doi.org/10.1162/tacl_a_00138
  16. Nivre J, de Marneffe MC, Ginter F, Goldberg Y, Hajic J, Manning CD, et al. Universal dependencies v1: a multilingual treebank collection. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) (Calzolari N, Choukri K, Declerck T, Goggi S, Grobelnik M, Maegaard B, et al., eds.), 2016 May 23-28, Portoroz, Slovenia. Paris: European Language Resources Association, 2016. pp. 1659-1666.