Abstract
Named Entity Recognition (NER) for open domain data is a critical task for the natural language process applications and attracts many research attention. However, the complexity of semantic dependencies and the sparsity of the context information make it difficult for identifying correct entities from the corpus. In addition, the lack of annotated training data makes impossible the prediction of fine-grained entity types for detected entities. To solve the above-mentioned problems in NER, we propose an extractor which takes both the near arguments and long dependencies of relations into consideration for the entities and relations mention discovery. We then employ distant-supervision methods to automatically label mention types of training data sets and a neural network model is proposed for learning the type classifier. Empirical studies on two real-world raw text corpus, NYT and YELP, demonstrate that our proposed NER approach outperforms the existing models.
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Notes
- 1.
These five labels are introduced in Stanford Dependency notations. http://nlp.stanford.edu/software/dependencies_manual.pdf.
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Acknowledgments
This work is supported partly by the National Natural Science Foundation of China (No. 61772059, 61602023 and 61421003), by the Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), by State Key Laboratory of Software Development Environment (No. SKLSDE-2018ZX-17), and by the Fundamental Research Funds for the Central Universities and the Beijing S&T Committee.
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Wu, J., Zhang, R., Deng, T., Huai, J. (2019). Named Entity Recognition for Open Domain Data Based on Distant Supervision. In: Zhu, X., Qin, B., Zhu, X., Liu, M., Qian, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding. CCKS 2019. Communications in Computer and Information Science, vol 1134. Springer, Singapore. https://doi.org/10.1007/978-981-15-1956-7_17
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DOI: https://doi.org/10.1007/978-981-15-1956-7_17
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