基于Bi-LSTM 的多层面隐喻识别方法

以双向长短期记忆网络(Bi-LSTM)为核心,结合多层卷积神经网络以及单向长短期记忆网络构建了多层面隐喻识别模型.基于多特征协同作用的思想,利用依存关系特征、语义特征、词性特征等多特征融合输入方法,丰富了模型的学习信息.为降低信息干扰,利用基于统计学的规范化文本输入方法提升模型识别效果.在英文语料词层面和句层面实验中,各个特征均表现出明显的正向作用.裁剪和填充处理及多特征协调作用在英文语料词层面研究中使F_1值分别提升2.5%和5.1%,在句层面研究中F_1值分别提升3.1%和1.9%.在中文语料句层面实验中,最优效果的F_1值可达88.8%.

Multi-level metaphor detection method based on Bi-LSTM

Taking Bi-directional long short-term memory network (Bi-LSTM) as the core, combining with multi convolutional neural network layers and unidirectional long short-term memory network, a multi-level metaphor recognition model is built. Based on the idea of multi-feature synergism, the learning information of the model is enriched by using the methods of inputting multi-feature such as dependency feature, semantic feature and part-of-speech feature in parallel. In order to reduce information interference, a standardized text input method based on statistics is used to improve the recognition effect of the model. In the experiments on word level and sentence level of English corpus, each feature has obvious positive effect. In the word level research of English corpus, the F1-scores of croping and filling treatment, multi-feature synergism increase by 2.5% and 5.1% respectively, while in the sentence level research, the F1-scores increase by 3.1% and 1.9% respectively. In the sentence level experiment of Chinese corpus, the F1-score of the optimal effect can reach 88.8%.