Intent Classification by the Use of Automatically Generated Knowledge Graphs

Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers’ goals. Although intent classification performs well in general settings, domain-specific user goals can still present a challenge for this task. To address this challenge, we automa...

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Bibliographic Details
Published inInformation (Basel) Vol. 14; no. 5; p. 288
Main Authors Arcan, Mihael, Manjunath, Sampritha, Robin, Cécile, Verma, Ghanshyam, Pillai, Devishree, Sarkar, Simon, Dutta, Sourav, Assem, Haytham, McCrae, John P., Buitelaar, Paul
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2023
Subjects
Algorithms
Analysis
Automation
Classification
Computational linguistics
Datasets
Graphs
intent classification
Interactive computer systems
knowledge graph generation
Knowledge representation
Language
Language processing
named entity extraction
Natural language
Natural language interfaces
relation extraction
Semantic relations
Semantics
term extraction
United Kingdom
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Summary:Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers’ goals. Although intent classification performs well in general settings, domain-specific user goals can still present a challenge for this task. To address this challenge, we automatically generate knowledge graphs for targeted data sets to capture domain-specific knowledge and leverage embeddings trained on these knowledge graphs for the intent classification task. As existing knowledge graphs might not be suitable for a targeted domain of interest, our automatic generation of knowledge graphs can extract the semantic information of any domain, which can be incorporated within the classification process. We compare our results with state-of-the-art pre-trained sentence embeddings and our evaluation of three data sets shows improvement in the intent classification task in terms of precision.
ISSN:2078-2489
2078-2489
DOI:10.3390/info14050288