A Question Answering Method of Knowledge Graph Based on BiLSTM-CRF and Seq2Seq

In natural language processing, intelligent question answering based on knowledge graph has received great attention. In the previous knowledge base question answering, the traditional word vector is difficult to express the text semantic information, and the cyclic neural network is easy to cause g...

Full description

Saved in:
Bibliographic Details
Published in2022 11th International Conference of Information and Communication Technology (ICTech) pp. 1 - 5
Main Authors Zhang, Yiying, Ma, Caixia, He, Yeshen, Liang, Kun, Wu, Yannian, Liu, Zhu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2022
Subjects
Analytical models
BiLSTM-CRF model
entity recognition
Fitting
intelligent Q&A
Knowledge based systems
Knowledge engineering
knowledge graph
model building
Neural networks
Question answering (information retrieval)
Semantics
Online AccessGet full text

Cover

More Information
Summary:In natural language processing, intelligent question answering based on knowledge graph has received great attention. In the previous knowledge base question answering, the traditional word vector is difficult to express the text semantic information, and the cyclic neural network is easy to cause gradient disappearance and gradient explosion. At the same time, it is lack of comprehensive consideration of text context information. This paper proposes an intelligent Q & A method based on knowledge graph, which uses BiLSTM-CRF model to realize entity recognition. The intelligent Q & A model is constructed based on Seq2Seq, and the above methods are verified by taking the intelligent Q & A as an example, which effectively improves the accuracy of intelligent Q & A.
DOI:10.1109/ICTech55460.2022.00017