| 摘要(英) |
Named Entity Recognition (NER) focuses on locating the mentions of name entities and classifying their types, usually referring to proper nouns such as persons, places, organizations, dates, and times. The NER results can be used as the basis for relationship extraction, event detection and tracking, knowledge graph building, and question answering system. NER studies usually regard this research topic as a sequence labeling problem and learns the labeling model through the large-scale corpus. We propose a ME-GGSNN (Multiple Embeddings enhanced Gated Graph Sequence Neural Networks) model for Chinese healthcare NER. We derive a character representation based on multiple embeddings in different granularities from the radical, character to word levels. An adapted gated graph sequence neural network is involved to incorporate named entity information in the dictionaries. A standard BiLSTM-CRF is then used to identify named entities and classify their types in the healthcare domain.
We firstly crawled articles from websites that provide healthcare information, online health-related news and medical question/answer forums. We then randomly selected partial sentences to retain content diversity. It includes 30,692 sentences with a total of around 1.5 million characters or 91.7 thousand words. After manual annotation, we have 68,460 named entities across 10 entity types: body, symptom, instrument, examination, chemical, disease, drug, supplement, treatment, and time. Based on further experiments and error analysis, our proposed method achieved the best F1-score of 75.69% that outperforms previous models including the BiLSTM-CRF, BERT, Lattice, Gazetteers, and ME-CNER. In summary, our ME-GGSNN model is an effective and efficient solution for the Chinese healthcare NER task. |
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