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

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.