聊天机器人中用户就医意图识别方法

传统的聊天机器人中用户意图识别一般采用基于模板匹配或人工特征集合等方法，针对其费时费力而且扩展性不强的问题，并结合医疗领域聊天文本的特点，提出了基于短文本主题模型（BTM）和双向门控循环单元（BiGRU）的意图识别模型。该混合模型将用户就医意图识别看作分类问题，使用主题特征，首先通过BTM对用户聊天文本逐句进行主题挖掘并量化，然后送入BiGRU进行完整上下文学习得到连续语句最终表示，最后通过分类完成用户就医意图识别。对爬取的语料进行实验，BTM-BiGRU方法明显优于传统的支持向量机（SVM）等方法，其F值更是高出目前较好的卷积长短期记忆组合神经网络（CNN-LSTM）近1.5个百分点。实验结果表明，在本任务上该混合模型重点考虑研究对象的特点，能有效提高意图识别的准确率。

Identification method of user's medical intention in chatting robot

Traditional user intention recognition methods in chatting robot are usually based on template matching or artificial feature sets. To address the problem that those methods are difficult, time-consuming but have a week extension, an intention recognition model based on Biterm Topic Model (BTM) and Bidirectional Gated Recurrent Unit (BiGRU) was proposed with considering the features of the chatting texts about health. The identification of user's medical intention was regarded as a classification problem and topic features were used in the hybrid model. Firstly, the topic of user's every chatting sentence was mined by BTM with quantification. Then last step's results were fed into BiGRU to do context-based learning for getting the final representation of user's continuous statements. At last, the task was finished by making classification. In the comparison experiments on crawling corpus, the BTM-BiGRU model obviously outperforms to other traditional methods such as Support Vector Machine (SVM), even the F value approximately increses by 1.5 percentage points compared to the state-of-the-art model combining Convolution Neural Network and Long-Short Term Memory Network (CNN-LSTM). Experimental results show that the proposed method can effectively improve the accuracy of the intention recognition focusing on characteristics of the study.