基于特征融合的中文简历解析方法研究

针对基于规则和统计的传统中文简历解析方法效率低、成本高、泛化能力差的缺点,提出一种基于特征融合的中文简历解析方法,即级联Word2Vec生成的词向量和用BLSTM(Bidirectional Long Short-Term Memory)建模字序列生成的词向量,然后再结合BLSTM和CRF(Conditional Random Fields)对中文简历进行解析(BLSTM-CRF)。为了提高中文简历解析的效率,级联包含字序列信息的词向量和用Word2Vec生成的词向量,融合成一个新的词向量表示;再由BLSTM强大的学习能力融合词的上下文信息,输出所有可能标签序列的分值给CRF层;再由CRF引入标签之间约束关系求解最优序列。利用梯度下降算法训练神经网络,使用预先训练的词向量和Dropout优化神经网络,最终完成对中文简历的解析工作。实验结果表明,所提的特征融合方法优于传统的简历解析方法。

Research of Chinese Resume Analysis Based on Feature Fusion

It’s typical for the Chinese resume analysis to apply the rule-based and statistical-based methods, suffering from the low efficiency, high cost and poor generalization ability. This paper proposes a Chinese resume analysis method based on feature fusion model. The concatenation of the word vectors generated by Word2Vec and the word representation is generated from BLSTM neural network, then the text resume is analyzed by intergrating the BLSTM and CRF model（BLSTM-CRF）. In order to improve the efficiency of Chinese resume resolution, the two vectors are concatenated into a new word representation. Furthermore, the BLSTM layer is used to fuse the contextual information of the words to be marked, and then the values of all possible tag sequences are exported to the CRF layer. Finally, according to the constraints of the front and rear labels, the CRF is utilized to obtain the optimal labeling sequence. All of the neural networks are trained by the gradient descent algorithm and are optimized by the pretrained word embeddings and Dropout. The experimental results show that the feature fusion method is superior to the traditional resume analysis schemes.