Abstract
Unsupervised features based on word representations such as word embeddings and word collocations have shown to significantly improve supervised NER for English. In this work we investigate whether such unsupervised features can also boost supervised NER in Spanish. To do so, we use word representations and collocations as additional features in a linear chain Conditional Random Field (CRF) classifier. Experimental results (82.44 % F-score on the CoNLL-2002 corpus) show that our approach is comparable to some state-of-art Deep Learning approaches for Spanish, in particular when using cross-lingual word representations.
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References
Bhattarai, B.: Inducing cross-lingual word representations. Master’s thesis, Multimodal Computing and Interaction, Machine Learning for Natural Language Processing. Universität des Saarlandes (2013)
Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. In: Chiarcos, C., de Castilho, E., Stede, M. (eds.) Von der Form zur Bedeutung: Texte automatisch verarbeiten/From Form to Meaning: Processing Texts Automatically, Proceedings of the Biennial GSCL Conference 2009, pp. 31–40. Gunter Narr Verlag, Tübingen (2009)
Brown, P.F., deSouza, P.V., Mercer, R.L., Pietra, V.J.D., Lai, J.C.: Class-based n-gram models of natural language. Comput. Linguist. 18(4), 467–479 (1992)
Cardellino, C.: Spanish Billion Words Corpus and Embeddings (2016). http://crscardellino.me/SBWCE/
Carreras, X., Màrques, L., Padró, L.: Named entity extraction using adaboost. In: Proceedings of CoNLL-2002, Taipei, Taiwan, pp. 167–170 (2002)
Collobert, R., Weston, J.: Deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 160–167. ACM, New York (2008)
dos Santos, C., Guimarães, V.: Boosting named entity recognition with neural character embeddings. In: Proceedings of the Fifth Named Entity Workshop, July, Beijing, China, pp. 25–33. Association for Computational Linguistics (2015)
Faruqui, M., Padó, S.: Training and evaluating a German named entity recognizer with semantic generalization. In: Proceedings of KONVENS 2010, Saarbrücken, Germany (2010)
Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL 2005, Stroudsburg, PA, USA, pp. 363–370. Association for Computational Linguistics (2005)
Gillick, D., Brunk, C., Vinyals, O., Subramanya, A.: Multilingual Language Processing from Bytes. ArXiv e-prints (2015)
Guo, J., Che, W., Wang, H., Liu, T.: Revisiting embedding features for simple semi-supervised learning. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), October, Doha, Qatar, pp. 110–120. Association for Computational Linguistics (2014)
Lample, G., Ballesteros, M., Kawakami, K., Subramanian, S., Dyer, C: Neural architectures for named entity recognition. In: Proceedings of NAACL-HLT (NAACL 2016), San Diego, US (2016)
Liang, P.: Semi-supervised learning for natural language. Master’s thesis, Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology (2005)
Mikolov, T., Chen, K., Corrado, G., Dean. J.: Efficient estimation of word representations in vector space. CoRR, abs/1301.3781 (2013a)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26, pp. 3111–3119 (2013b)
Okazaki, N.: CRFsuite: a fast implementation of conditional random fields (CRFs) (2007)
Passos, A., Kumar, V., McCallum, A.: Lexicon infused phrase embeddings for named entity resolution. In: Proceedings of the Eighteenth Conference on Computational Natural Language Learning, June, Ann Arbor, Michigan, pp. 78–86. Association for Computational Linguistics (2014)
Poulsen, S.: Collocations as a language resource. A functional and cognitive study in English phraseology. Ph.D. dissertation, Institute of Language and Communication. University of Southern Denmark (2005)
Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning, CoNLL 2009, Stroudsburg, PA, USA, pp. 147–155. Association for Computational Linguistics (2009)
Sculley, D.: Combined regression and ranking. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 979–988. ACM, New York (2010)
Sutton, C., McCallum, A.: An introduction to conditional random fields. Found. Trends Mach. Learn. 4(4), 267–373 (2012)
Tjong Kim Sang, E.F.: Language-independent named entity recognition. In: Proceedings of the 6th Conference on Natural Language Learning - vol. 20, COLING-02, Stroudsburg, PA, USA, pp. 1–4. Association for Computational Linguistics (2002)
Turian, J., Ratinov, L., Bengio, Y.: A simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010, Stroudsburg, PA, USA, pp. 384–394. Association for Computational Linguistics (2010)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML 1997, San Francisco, CA, USA, pp. 412–420. Morgan Kaufmann Publishers Inc. (1997)
Yang, Z., Salakhutdinov, R., Cohen, W.: Multi-task cross-lingual sequence tagging from scratch. CoRR, abs/1603.06270 (2016)
Yu, M., Zhao, T., Dong, D., Tian, H., Yu, D.: 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, pp. 563–568 (2013)
Yu, M., Zhao, T., Bai, Y., Tian, H., Yu. D.: 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), Sofia, Bulgaria, pp. 312–317. Association for Computational Linguistics (2013)
Acknowledgments
We are grateful to the Data and Web Science Group at University of Mannheim. Special thanks to Heiner Stuckenschmidt and Simone Ponzetto for their contributions and comments. This work was supported by the Master Program in Computer Science at Universidad Católica San Pablo and the Peruvian National Fund of Scientific and Technological Development through grant number 011-2013-FONDECYT.
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Copara, J., Ochoa, J., Thorne, C., Glavaš, G. (2016). Conditional Random Fields for Spanish Named Entity Recognition Using Unsupervised Features. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_15
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