Abstract
Extracting named entities out of unstructured text data is an important problem in natural language processing, with applications in tasks such as sentiment analysis, information retrieval and answer selection in question answering. For identifying named entities, many methods have been developed ranging from knowledge-based methods to supervised machine learning methods. Recently, deep learning models have shown better performance than conventional methods in Named Entity Recognition (NER) tasks. However, it remains unclear how the state-of-the-art neural NER models that have shown superior performance on benchmark datasets perform on other domains. This paper aims to analyze the performance of a state-of-the-art neural NER model in identifying the named entities from judicial text data. The experimentally obtained results show that the model achieved very good results in identifying person names, location names and average result in organization names.
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Anu, T., Sangeetha, S.: TIEx-A tool for extracting structured and semantic information from text document. In: Proceedings of the Fourth International Conference on Business Analytics and Intelligence, pp. 1026–1032 (2016)
Bach, N., Badaskar, S.: A review of relation extraction. Literature review for language and statistics II. In: Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 541–550 (2007)
Batra, S., Rao, D.: Entity based sentiment analysis on twitter. Science 9(4), 1–12 (2010)
Bender, O., Och, F.J., Ney, H.: Maximum entropy models for named entity recognition. In: Proceedings of the Seventh Conference on Natural language Learning at HLT-NAACL 2003, vol. 4, pp. 148–151. Association for Computational Linguistics (2003)
Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNS. arXiv preprint arXiv:1511.08308 (2015)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Dozier, C., Kondadadi, R., Light, M., Vachher, A., Veeramachaneni, S., Wudali, R.: Named entity recognition and resolution in legal text. In: Semantic Processing of Legal Texts, pp. 27–43. Springer (2010)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)
Isozaki, H., Kazawa, H.: Efficient support vector classifiers for named entity recognition. In: Proceedings of the 19th International Conference on Computational Linguistics, vol. 1, pp. 1–7. Association for Computational Linguistics (2002)
Klinger, R.: Automatically selected skip edges in conditional random fields for named entity recognition. In: Proceedings of the International Conference Recent Advances in Natural Language Processing 2011, pp. 580–585 (2011)
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)
Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNS-CRF. arXiv preprint arXiv:1603.01354 (2016)
Marcińczuk, M.: Automatic construction of complex features in conditional random fields for named entities recognition. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, pp. 413–419 (2015)
Mihalcea, R., Moldovan, D.: Document indexing using named entities. Stud. Inform. Control. 10(1), 21–28 (2001)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). URL http://www.aclweb.org/anthology/D14-1162
Rajpurohit, J., Sharma, T.K., Abraham, A., Vaishali: Glossary of metaheuristic algorithms. Int. J. Comput. Inf. Syst. Ind. Manag. Appl., 181–205 (2017)
Rathore, A.S., Arjaria, S., Khandelwal, S., Thorat, S., Kulkarni, V.: Movie rating system using sentiment analysis. In: Soft Computing: Theories and Applications, pp. 85–98. Springer (2019)
Rei, M., Crichton, G.K., Pyysalo, S.: Attending to characters in neural sequence labeling models. arXiv preprint arXiv:1611.04361 (2016)
Santos, C.N.D., Guimaraes, V.: Boosting named entity recognition with neural character embeddings. arXiv preprint arXiv:1505.05008 (2015)
Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, vol. 4, pp. 142–147. Association for Computational Linguistics (2003)
Zhou, G., Su, J.: Named entity recognition using an HMM-based chunk tagger. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 473–480. Association for Computational Linguistics (2002)
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Thomas, A., Sangeetha, S. (2020). Performance Analysis of the State-of-the-Art Neural Named Entity Recognition Model on Judicial Domain. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_14
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