注意力机制的BiLSTM模型在招聘信息分类中的应用

BiLSTM Model of Attention Mechanism Application in Recruitment Information Classification

目前IT招聘信息分类中传统算法存在长距离依赖,且无法突出IT岗位关键词对文本分类特征影响等问题.本文通过训练双向长短期记忆网络BiLSTM与注意力机制相结合的多层文本分类模型,将其应用到招聘信息分类中.该模型包括One-hot词向量输入层、BiLSTM层、注意力机制层和输出层.其中One-hot层构建招聘词典,节省了大量训练词向量时间, BiLSTM层可获取更多上下文不同距离的语义信息,注意力机制层对经过BiLSTM层编码数据进行加权转变可提升序列化学习任务.实验表明:基于该模型的IT招聘信息分类准确率达到93.36%,与其他模型对比,提高约2%.该模型更有针对性的分析不同岗位对就业者能力的要求,实现了不同岗位招聘信息的分类,对高校毕业生就业指导有重要意义.

At present, traditional algorithms in IT recruitment information classification have long-distance dependence, and cannot highlight the impact of IT job keywords on text classification features. In this study, a multi-layer text classification model combining two-way long-term and short-term memory network BiLSTM and attention mechanism is applied to the classification of recruitment information. The model includes the one-hot word vector input layer, BiLSTM layer, attention mechanism layer, and output layer. One-hot layer builds a recruitment dictionary, which saves a lot of training word vector time;the BiLSTM layer can obtain more semantic information of different distances in the context;and the attention mechanism layer transforms the weights of the data encoded by BiLSTM enhancing the serialization learning task. The results show that the classification accuracy of IT recruitment information based on this model reaches 93.36%, which is about 2% higher than other models. The model analyzes the requirements of different positions on the ability of the employed in a more targeted manner, and realizes the classification of recruitment information in different positions, which is of great significance to the employment guidance of college graduates.