Spanish NER with Word Representations and Conditional Random Fields

Author

Copara Zea, Jenny Linet and Ochoa Luna, Jose Eduardo and Thorne, Camilo and Glavaš, Goran

Conference

Proceedings of the Sixth Named Entity Workshop

Year

2016

Figures & Tables

Table 6: CoNLL2002 Spanish Results. Top: results obtained by us. Middle: results obtained with other CRF-based approaches. Down: current Deep Learning-based state-of-the-art for Spanish NER.
Table 5: CoNLL-2002 Spanish Prototypes.
Figure 1: Linear chain-CRF with word representations as features. The upper nodes are the label sequences, the bottom white nodes are the word features in the model and the filled nodes are the word representations features included in our model.
Table 2: Brown cluster computed from SBW.
Table 3: Binarized embeddings from SBW for word “equipo”.
Table 4: Clustering embeddings from SBW for word “Maria”.
Table 1: Entities in CoNLL-2002 (Spanish).

Table of Contents

  • Abstract
  • 1 Introduction
  • 2 Related work
    • 2.1 Spanish NER
    • 2.2 Word Representations
  • 3 Word Representations for Spanish NER
  • 4 Experiments and Discussion
    • 4.1 NER Model
    • 4.2 Baseline Features
    • 4.3 CoNLL 2002 Spanish Corpus
    • 4.4 Word Representations
    • 4.5 Results
    • 4.6 Discussion
  • 5 Conclusions
  • Acknowledgments
  • References

References

  • 2Binod Bhattarai. 2013. Inducing cross-lingual word representations. Master’s thesis, Multimodal Computing and Interaction, Machine Learning for Natural Language Processing. Universität des Saarlandes.
  •  G. Bouma. 2009. Normalized (pointwise) mutual information in collocation extraction. In C. Chiarcos, E. de Castilho, and M. Stede, editors, Von der Form zur Bedeutung: Texte automatisch verarbeiten/ From Form to Meaning: Processing Texts Automatically, Proceedings of the Biennial GSCL Conference 2009, pages 31–40, Tübingen. Gunter Narr Verlag.
  • 3Peter F. Brown, Peter V. deSouza, Robert L. Mercer, Vincent J. Della Pietra, and Jenifer C. Lai.1992. Class-based n-gram models of natural language. Comput. Linguist., 18(4):467–479, December.View this Paper
  • 2Cristian Cardellino. 2016. Spanish Billion Words Corpus and Embeddings, March.
  •  2Xavier Carreras, Lluı́s Màrques, and Lluı́s Padró.2002. Named entity extraction using adaboost. In Proceedings of CoNLL-2002, pages 167–170. Taipei, Taiwan.View this Paper
  •  Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: DeepView this Paper
  •   neural networks with multitask learning. In Proceedings of the 25th International Conference on Machine Learning, ICML ’08, pages 160–167, New York, NY, USA. ACM.
  •   Cicero dos Santos and Victor Guimarães. 2015. Boosting named entity recognition with neural character embeddings. In Proceedings of the Fifth Named Entity Workshop, pages 25–33, Beijing, China, July. Association for Computational Linguistics.View this Paper
  •   Manaal Faruqui and Sebastian Padó. 2010. Training and evaluating a german named entity recognizer with semantic generalization. In Proceedings of KONVENS 2010, Saarbrücken, Germany.View this Paper
  •   Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. Incorporating non-local information into information extraction systems by gibbs sampling. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics,ACL ’05, pages 363–370, Stroudsburg, PA, USA. Association for Computational Linguistics.View this Paper
  •   D. Gillick, C. Brunk, O. Vinyals, and A. Subramanya.2015. Multilingual Language Processing From Bytes. ArXiv e-prints, November.View this Paper
  •   Jiang Guo, Wanxiang Che, Haifeng Wang, and Ting Liu. 2014. Revisiting embedding features for simple semi-supervised learning. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 110–120, Doha, Qatar, October. Association for Computational Linguistics.View this Paper
  •   Guillaume Lample, Miguel Ballesteros, Kazuya Kawakami, Sandeep Subramanian, and Chris Dyer.2016. Neural architectures for named entity recognition. In In proceedings of NAACL-HLT (NAACL 2016)., San Diego, US.View this Paper
  •   Percy Liang. 2005. Semi-supervised learning for natural language. Master’s thesis, Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology.
  •   Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. CoRR, abs/1301.3781.View this Paper
  •   Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013b. Distributed representations of words and phrases and their compo-sitionality. In C.j.c. Burges, L. Bottou, M. Welling,Z. Ghahramani, and K.q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 3111–3119.View this Paper
  •   Naoaki Okazaki. 2007. Crfsuite: a fast implementation of conditional random fields (crfs).
  •   Alexandre Passos, Vineet Kumar, and Andrew McCallum. 2014. Lexicon infused phrase embeddings forView this Paper
  •   named entity resolution. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning, pages 78–86, Ann Arbor, Michigan, June. Association for Computational Linguistics.
  •   Lev Ratinov and Dan Roth. 2009. Design challenges and misconceptions in named entity recognition. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning, CoNLL ’09,pages 147–155, Stroudsburg, PA, USA. Association for Computational Linguistics.View this Paper
  •  D. Sculley. 2010. Combined regression and ranking. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10, pages 979–988, New York,NY, USA. ACM.View this Paper
  • 2Charles Sutton and Andrew McCallum. 2012. An introduction to conditional random fields. Foundations and Trends in Machine Learning, 4(4):267–373.View this Paper
  • 2Erik F. Tjong Kim Sang. 2002. Introduction to the conll-2002 shared task: Language-independent named entity recognition. In Proceedings of the 6th Conference on Natural Language Learning - Volume 20, COLING-02, pages 1–4, Stroudsburg, PA, USA. Association for Computational Linguistics.View this Paper
  • 223Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: A simple and general method for semi-supervised learning. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL ’10, pages 384–394,Stroudsburg, PA, USA. Association for Computational Linguistics.View this Paper
  •  Yiming Yang and Jan O. Pedersen. 1997. A comparative study on feature selection in text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, ICML ’97, pages 412–420, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.View this Paper
  • 3Zhilin Yang, Ruslan Salakhutdinov, and William Cohen. 2016. Multi-task cross-lingual sequence tagging from scratch. CoRR, abs/1603.06270.View this Paper
  •  Mo Yu, Tiejun Zhao, Yalong Bai, Hao Tian, and Dianhai Yu. 2013a. Cross-lingual projections between languages from different families. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers),pages 312–317, Sofia, Bulgaria, August. Association for Computational Linguistics.View this Paper
  •   Mo Yu, Tiejun Zhao, Daxiang Dong, Hao Tian, and Dianhai Yu. 2013b. Compound embedding features for semi-supervised learning. In Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 9-14, 2013, Westin Peachtree Plaza Hotel, Atlanta, Georgia, USA,pages 563–568.View this Paper
+- Similar Papers (10)