本文主要是对在线问诊中产生的医疗文本进行命名实体识别的研究.使用在线医疗问答网站的数据,采用{B, I, O}标注体系构建数据集,抽取疾病、治疗、检查和症状四个医疗实体.以BiLSTM-CRF为基准模型,提出两种深度学习模型IndRNN-CRF和IDCNN...本文主要是对在线问诊中产生的医疗文本进行命名实体识别的研究.使用在线医疗问答网站的数据,采用{B, I, O}标注体系构建数据集,抽取疾病、治疗、检查和症状四个医疗实体.以BiLSTM-CRF为基准模型,提出两种深度学习模型IndRNN-CRF和IDCNN-BiLSTM-CRF,并在自构建数据集上验证模型的有效性.将新提出的两种模型与基准模型通过实验对比得出:模型IDCNN-BiLSTM-CRF的F1值0.8116,超过了BiLSTM-CRF的F1值0.8009, IDCNN-BiLSTM-CRF整体性能好于BiLSTM-CRF模型;模型IndRNN-CRF的精确率0.8427,但该模型在召回率上低于基准模型BiLSTM-CRF.展开更多
Structure features need complicated pre-processing, and are probably domain-dependent. To reduce time cost of pre-processing, we propose a novel neural network architecture which is a bi-directional long-short-term-me...Structure features need complicated pre-processing, and are probably domain-dependent. To reduce time cost of pre-processing, we propose a novel neural network architecture which is a bi-directional long-short-term-memory recurrent-neural-network（Bi-LSTM-RNN） model based on low-cost sequence features such as words and part-of-speech（POS） tags, to classify the relation of two entities. First, this model performs bi-directional recurrent computation along the tokens of sentences. Then, the sequence is divided into five parts and standard pooling functions are applied over the token representations of each part. Finally, the token representations are concatenated and fed into a softmax layer for relation classification. We evaluate our model on two standard benchmark datasets in different domains, namely Sem Eval-2010 Task 8 and Bio NLP-ST 2016 Task BB3. In Sem Eval-2010 Task 8, the performance of our model matches those of the state-of-the-art models, achieving 83.0% in F1. In Bio NLP-ST 2016 Task BB3, our model obtains F1 51.3% which is comparable with that of the best system. Moreover, we find that the context between two target entities plays an important role in relation classification and it can be a replacement of the shortest dependency path.展开更多
文摘本文主要是对在线问诊中产生的医疗文本进行命名实体识别的研究.使用在线医疗问答网站的数据,采用{B, I, O}标注体系构建数据集,抽取疾病、治疗、检查和症状四个医疗实体.以BiLSTM-CRF为基准模型,提出两种深度学习模型IndRNN-CRF和IDCNN-BiLSTM-CRF,并在自构建数据集上验证模型的有效性.将新提出的两种模型与基准模型通过实验对比得出:模型IDCNN-BiLSTM-CRF的F1值0.8116,超过了BiLSTM-CRF的F1值0.8009, IDCNN-BiLSTM-CRF整体性能好于BiLSTM-CRF模型;模型IndRNN-CRF的精确率0.8427,但该模型在召回率上低于基准模型BiLSTM-CRF.
基金Supported by the China Postdoctoral Science Foundation(2014T70722)the Humanities and Social Science Foundation of Ministry of Education of China(16YJCZH004)
文摘Structure features need complicated pre-processing, and are probably domain-dependent. To reduce time cost of pre-processing, we propose a novel neural network architecture which is a bi-directional long-short-term-memory recurrent-neural-network（Bi-LSTM-RNN） model based on low-cost sequence features such as words and part-of-speech（POS） tags, to classify the relation of two entities. First, this model performs bi-directional recurrent computation along the tokens of sentences. Then, the sequence is divided into five parts and standard pooling functions are applied over the token representations of each part. Finally, the token representations are concatenated and fed into a softmax layer for relation classification. We evaluate our model on two standard benchmark datasets in different domains, namely Sem Eval-2010 Task 8 and Bio NLP-ST 2016 Task BB3. In Sem Eval-2010 Task 8, the performance of our model matches those of the state-of-the-art models, achieving 83.0% in F1. In Bio NLP-ST 2016 Task BB3, our model obtains F1 51.3% which is comparable with that of the best system. Moreover, we find that the context between two target entities plays an important role in relation classification and it can be a replacement of the shortest dependency path.
