Clinical Name Entity Recognition using Conditional Random Field with Augmented Features
نویسنده
چکیده
In this paper, We presents a Chinese medical term recognition system submitted to the competition held by China Conference on Knowledge Graph and Semantic Computing. I compare the performance of Linear Chain Conditional Random Field (CRF) with that of Bi-Directional Long Short Term Memory (LSTM) with Convolutional Neural Network (CNN) and CRF layers performance and find that CRF with augmented features performs best with F1 0.927 on the offline competition dataset using cross-validation. Hence, this system was built by using a conditional random field model with linguistic features such as character identity, N-gram, and external dictionary features.
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