Abstract
Traditional methods for named entity recognition (NER) require heavy feature engineering to achieve high performance. We propose a novel neural network architecture for NER that detects word features automatically without feature engineering. Our approach uses word embedding as input, feeds them into a bidirectional long short-term memory (B-LSTM) for modeling the context within a sentence, and outputs the NER results. This study extends the neural network language model through B-LSTM, which outperforms other deep neural network models in NER tasks. Experimental results show that the B-LSTM with word embedding trained on a large corpus achieves the highest F-score of 0.9247, thus outperforming state-of-the-art methods that are based on feature engineering.
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This work was supported by the project of National Natural Science Foundation of China (No. 61471169).
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Ouyang, L., Tian, Y., Tang, H., Zhang, B. (2017). Chinese Named Entity Recognition Based on B-LSTM Neural Network with Additional Features. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10656. Springer, Cham. https://doi.org/10.1007/978-3-319-72389-1_22
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DOI: https://doi.org/10.1007/978-3-319-72389-1_22
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