增强句子嵌入的问答系统

2022 11th International Conference of Information and Communication Technology (ICTech)) Pub Date : 2022-02-01 DOI:10.1109/ICTech55460.2022.00109

Hongliang Wang, XinXin Lu

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引用次数: 1

摘要

为了提高问答系统对输入问题的语义理解能力，构建了一个基于知识表示的问答系统，该系统由命名实体识别和问题匹配两部分组成。采用Bert+BiLSTM+CRF的命名实体识别方法，采用本文提出的BGCNN模型进行问题匹配。BGCNN是一个结合Bert、神经网络和暹罗网络的模型。系统在财务数据集上的平均F1值为0.9007，与之前的模型相比，这是一个不小的进步。

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Question Answering System with Enhancing Sentence Embedding

In order to improve the semantic understanding of the input question in the question answering system, a question answering system based on knowledge representation is constructed, which is composed of named entity recognition and question matching. The named entity recognition method based on Bert+BiLSTM+CRF is used, and the BGCNN model proposed in this paper is used for question matching. BGCNN is a model combining Bert, neural network and Siamese network. The average F1 value of the system on the financial data set is 0.9007, which is not a small improvement compared with the previous model.

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来源期刊

2022 11th International Conference of Information and Communication Technology (ICTech))

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