Abstract
In Recent years, medical text mining has been an active research field because of its significant application potential, and information extraction (IE) is an essential step in it. This paper focuses on the medical IE, whose aim is to extract the pivotal contents from the medical texts such as drugs, treatments and so on. In existing works, introducing side information into neural network based Conditional Random Fields (CRFs) models have been verified to be effective and widely used in IE. However, they always neglect the traditional attributes of data, which are important for the IE performance, such as lexical and morphological information. Therefore, starting from the raw data, a novel attribute embedding based MC-BLSTM-CRF model is proposed in this paper. We first exploit a bidirectional LSTM (BLSTM) layer to capture the context semantic information. Meanwhile, a multi-channel convolutional neural network (MC-CNN) layer is constructed to learn the relations between multiple attributes automatically and flexibly. And on top of these two layers, we introduce a CRF layer to predict the output labels. We evaluate our model on a Chinese medical dataset and obtain the state-of-the-art performance with \(80.71\%\) F1 score.
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Acknowledgement
This research is supported by the National Natural Science Foundation of China under the grant No. U1633103 and 61502499, the Science and Technology Planning Project of Tianjin under the grant No. 17ZXRGGX00170, the Natural Science Foundation of Tianjin under the grant No. 18JCYBJC15800, and the Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China under the grant No. CAAC-ITRB-201601.
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Liu, J., Chen, S., He, Z., Chen, H. (2018). Learning BLSTM-CRF with Multi-channel Attribute Embedding for Medical Information Extraction. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11108. Springer, Cham. https://doi.org/10.1007/978-3-319-99495-6_17
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DOI: https://doi.org/10.1007/978-3-319-99495-6_17
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