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

Keyphrases

• Intention Recognition 100%
• Word Embedding 100%
• Density-based Clustering Method 100%
• Embedding Features 100%
• Classification Model 66%
• Data Sparseness 66%
• Classification Performance 33%
• User Intention 33%
• Training Data 33%
• Utterance 33%
• Lexical Features 33%
• Model Performance 33%
• Task Interdependence 33%
• Natural Language Understanding 33%
• Intention Analysis 33%
• Understanding Users 33%
• Embedding Space 33%
• Appropriate Responses 33%
• System Understanding 33%
• Word Frequency Distribution 33%
• Embedding Vector 33%
• Dialogue Emotion Recognition 33%
• Dialogue Act Recognition 33%
• Dialogue Systems 33%
• Cosine Similarity Measure 33%

Computer Science

• Clustering Method 100%
• Word Embedding 100%
• Classification Models 66%
• Experimental Result 33%
• Classification Performance 33%
• Training Data 33%
• Natural-Language Understanding 33%
• Cosine Similarity 33%
• Performance Model 33%
• Embedding Space 33%
• Similarity Score 33%
• Dialog System 33%