Joint Model of Intelligent Q&A Intent Recognition Based on Knowledge Graph

被引：1

作者：

Ma, Zili ^{[1
]}

Wang, Shuying ^{[1
]}

Zhang, Haizhu ^{[2
]}

Li, Rong ^{[2
]}

机构：

[1] School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu

[2] School of Mechanical Engineering, Southwest Jiaotong University, Chengdu

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 06期

关键词：

intelligent Q & A; intention recognition; joint model;

D O I：

10.3778/j.issn.1002-8331.2206-0419

中图分类号：

学科分类号：

摘要：

Aiming at the situation that the existing joint model of intention recognition is prone to identify domain entities and question classification errors in the question answering of professional domain knowledge atlas, a joint model of intention recognition combined with domain knowledge atlas is proposed. The model has three steps. The ontology labels corresponding to entities in the domain knowledge map and the relationships between ontologies are imported into the training data set to form the knowledge text containing ontology labels and the knowledge text map containing additional ontology relationships. The knowledge text containing ontology tags is transformed into embedded representation through character level embedding and location information embedding, and the entity relationship visual matrix is created according to the knowledge text graph to clarify the correlation degree of each component of the knowledge text. The embedded representation and entity relationship visual matrix are input into the model coding layer for model training. Taking the high-speed train domain knowledge map as an example, through the verification of accuracy and recall, the model trained by this method has achieved better performance in the intention recognition task of the high-speed train domain question and answer datasets. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press.

引用

页码：171 / 178

页数：7

共 14 条

[1]

HAN D F, TURDY TOHETI, ASKAR HAMDULLA, Surrey on question classification method in question answering system, Computer Engineering and Applications, 57, 6, pp. 10-21, (2021)

[2]

LIU J, LI Y L., Review of intent detection methods in human-machine dialogue system, Computer Engineering and Applications, 55, 12, pp. 1-7, (2019)

[3]

HOU L X, LI Y L, LI C C., Review of research on task-oriented spoken language understanding, Computer Engineering and Applications, 55, 11, pp. 7-15, (2019)

[4]

GOO C W, GAO G, HSU Y K, Et al., Slot-gated modeling for joint slot filling and intent prediction, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, (2018)

[5]

LI H Y, ZHAO M, YU W Q., A multi-attention RNN-based relation linking approach for question answering over knowledge base, Journal of Southeast University(English Edition), 36, 4, pp. 385-392, (2020)

[6]

PENG M, YAO Y L, XIE Q Q, Et al., Knowledge representation learning for joint structural and textual embedding via attention-based CNN, Journal of Chinese Information Processing, 33, 2, pp. 51-58, (2019)

[7]

CHEN Q, ZHUO Z, WANG W., BERT for joint intent classification and slot filling, (2019)

[8]

GANGADHARAIAH R, NARAYANASWAMY B., Joint multiple intent detection and slot labeling for goal-oriented dialog, Proceedings of the 2019 Conference of the North, (2019)

[9]

SUN Y, WANG S H, LI Y K., ERNIE:enhanced representation through knowledge integration, (2019)

[10]

HOU L L, LI S K., Winter Olympic Q & A system based on knowledge map,TF-IDF and BERT model, CAAI Transactions on Intelligent Systems, 16, 4, pp. 819-826, (2021)

← 1 2 →