@inproceedings{eb53fcf8ca9247c0b3b89ed0e4b4777c,
title = "An intention domain classification method based on concept map and language model",
abstract = "The intention domain classification is one of the fundamental issues in the dialogue system. In view of the diversity and variations of users inputs in the dialogue system and the difficulty of obtaining large-scale corpus for intention domain classification tasks, this paper proposes an intention domain classification method based on concept map and language model, which combines concept maps with machine learning models. Firstly, the concept map is used to carry out a higher level conceptual abstraction (i.e. knowledge)of the users' inputs. Then the most suitable abstraction for linguistic characteristics are selected by a language model, and ultimately it is concatenated with the original input as the new input for a fastText model to perform intent domain classification. The macro-F1 value of the method on the SMP2017-ECDT data corpus reaches 94.21.",
keywords = "Concept Map, Dialogue Understanding, FastText, Intention Domain Classification, Knowledge Map, Language Model, Spoken Dialogue System",
author = "Bing Xu and Jingjing Zhao and Huaxing Shi and Muyun Yang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018 ; Conference date: 28-07-2018 Through 30-07-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/FSKD.2018.8687168",
language = "英语",
series = "ICNC-FSKD 2018 - 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "184--189",
editor = "Zheng Xiao and Lipo Wang and Guoqing Xiao and Xiong Ning and Kenli Li and Maozhen Li",
booktitle = "ICNC-FSKD 2018 - 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery",
address = "美国",
}