Abstract:

To address the difficulty of word boundary recognition stemming from nested entities in Chinese medical texts， as well as significant semantic information loss in existing Lattice-LSTM structures with integrated lexical features， an adaptive lexical information enhancement model for Chinese Medical Named Entity Recognition （MNER） was proposed. First， the BiLSTM （Bi-directional Long-Short Term Memory） network was utilized to encode the contextual information of the character sequence and capture the long-distance dependencies. Next， potential word information of each character was modeled as character-word pairs， and the self-attention mechanism was utilized to realize internal interactions between different words. Finally， a lexicon adapter based on bilinear-attention mechanism was used to integrate lexical information into each character in the text sequence， enhancing semantic information effectively while fully utilizing the rich boundary information of words and suppressing words with low correlation. Experimental results demonstrate that the average F1 value of the proposed model increases by 1.37 to 2.38 percentage points compared to the character-based baseline model， and its performance is further optimized when combined with BERT.

Key words: Medical Named Entity Recognition (MNER), Chinese medical text, lexicon adapter, self-attention mechanism, Bi-directional Long Short-Term Memory (BiLSTM) network

摘要：

针对中文医学文本实体嵌套导致的单词边界识别困难问题以及现有栅格结构集成词汇特征所面临的语义信息损失严重的情况，提出一种用于中文医学命名实体识别（MNER）的自适应词汇信息增强模型。首先，利用双向长短期记忆（BiLSTM）网络编码字符序列的上下文信息并捕捉较长距离的依赖关系；然后，对字符序列中每个字符的潜在单词信息进行字词对建模，采用自注意力机制实现不同单词之间的内部交互；最后，通过基于双线性注意力机制的词汇适配器将词汇信息集成到文本序列中的每个字符中，有效增强语义信息的同时充分利用单词丰富的边界信息，并抑制相关性低的单词。实验结果表明，所提模型与基于字符的基线模型相比，平均F1值分别提升了1.37~2.38个百分点，并在结合BERT后取得了最优的效果。

关键词: 医学命名实体识别, 中文医学文本, 词汇适配器, 自注意力机制, 双向长短期记忆网络