An Integrated Method of Semantic Parsing and Information Retrieval for Knowledge Base Question Answering

被引：1

作者：

Zhen, Shiqi ^{[1
]}

Yi, Xianwei ^{[1
]}

Lin, Zhishu ^{[1
]}

Xiao, Weiqi ^{[1
]}

Su, Haibo ^{[1
]}

Liu, Yijing ^{[1
]}

机构：

[1] Cognit Intelligence LabPercent Technol, Wuhan 430073, Peoples R China

来源：

CCKS 2021 - EVALUATION TRACK | 2022年 / 1553卷

关键词：

KBQA; Information retrieval; Semantic parsing;

D O I：

10.1007/978-981-19-0713-5_6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

For the task of open domain Knowledge Base Question Answering (KBQA) in CCKS 2021, we propose an integrated method, which complementarily combines a more generalized information retrieval model and a more accurate semantic parsing model without manual involvement of templates. Our method achieves the averaged F1-score of 78.52% on the final test data, and ranks third in the KBQA task of CCKS 2021.

引用

页码：44 / 51

页数：8

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