A novel density-based clustering method using word embedding features for dialogue intention recognition

Jungsun Jang, Yeonsoo Lee, Seolhwa Lee, Dongwon Shin, Dongjun Kim, Haechang Rim

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


In dialogue systems, understanding user utterances is crucial for providing appropriate responses. Various classification models have been proposed to deal with natural language understanding tasks related to user intention analysis, such as dialogue acts or emotion recognition. However, models that use original lexical features without any modifications encounter the problem of data sparseness, and constructing sufficient training data to overcome this problem is labor-intensive, time-consuming, and expensive. To address this issue, word embedding models that can learn lexical synonyms using vast raw corpora have recently been proposed. However, the analysis of embedding features is not yet sufficient to validate the efficiency of such models. Specifically, using the cosine similarity score as a feature in the embedding space neglects the skewed nature of the word frequency distribution, which can affect the improvement of model performance. This paper describes a novel density-based clustering method that efficiently integrates word embedding vectors into dialogue intention recognition. Experimental results show that our proposed model helps overcome the data sparseness problem seen in previous classification models and can assist in improving the classification performance.

Original languageEnglish
Pages (from-to)2315-2326
Number of pages12
JournalCluster Computing
Issue number4
Publication statusPublished - 2016 Dec 1


  • Density-based clustering
  • Dialogue Act
  • Emotion recognition
  • Word embedding

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications


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