实体情感极性识别是一项细粒度情感分析任务,旨在判断给定实体的情感极性。针对金融领域中同一文本中存在多个实体这一问题,该文提出了一种基于交互注意力的双图卷积网络的金融实体情感极性识别方法(ASynSemGCN)。该方法利用预训练模型RoBERTa-wwm-ext,结合实体对句子进行初始表示,再通过多头注意力建立实体与句子之间的交互信息表示。在此基础上,分别利用语法图卷积网络(SynGCN)和语义图卷积网络(SemGCN)对句子进行句法和语义的深层表示,最后,将实体的深层表示、实体字级嵌入表示以及句子嵌入表示拼接,通过全连接层对实体进行情感极性判别。在自建的金融实体情感数据集上进行实验,实验结果表明,该文提出的方法对于金融实体情感极性识别是有效的。
Abstract
Entity sentiment polarity identification is a fine-grained sentiment analysis task that aims to determine the sentiment polarity in given entity. To address such issues as the existence of multiple entities in the same text in the financial field, this thesis proposes a sentiment identification polarity method for financial entities based on interactive attention mechanism with two-graph convolutional network (ASynSemGCN) . In the proposed method, a sentence is initially represented in combination with entities by using the RoBERTa-wwm-ext, and then the interactive information between entities and sentences is established through multi-head attention mechanism. On this basis, we use syntactic graph convolutional network (SynGCN) and semantic graph convolutional network (SemGCN) to capture the syntax and semantics of sentences, respectively. Finally, we concatenate the deep representation of entities, the character embedding of the entity and the sentence embedding to identify entities sentiment polarity by using the full connection layer. Experimental results show that the proposed method is effective for the sentiment polarity identification of financial entities on self-constructed entity financial sentiment dataset.
关键词
实体情感极性 /
金融实体 /
交互注意力 /
双图卷积网络
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Key words
entity sentiment polarity /
financial entity /
interactive attention /
bigraph convolutional network
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基金
国家自然科学基金(62106130,62076158,62072294);山西省基础研究计划项目(20210302124084);山西省高校科技创新计划项目(2021L284);山西省研究生教育创新项目(2022Y128)
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