Keyword-Text Graph Representation for Short Text Classification

Abstract

Short text classification is an essential task in Natural Language Processing. This task is widely applied to many applications, such as spam filtering, question-answering, artificial conversational agent, sentiment analysis, review mining, etc. Short texts usually encounter a great challenge for classification due to data sparseness as they do not provide sufficient contextual information. In this paper, we introduce Keyword-Text Graph Convolutional Networks (KwTGCN) for short-text classification. We also propose a method to identify keywords by estimating word distribution over different categories. These category keywords are then used to build a special keyword-text graph of short-text corpus. We employ Graph Convolutional Network (GCN) and our keyword-text graph to generate the representation of short-text corpus based on the relations of document-keyword and document-word as well as the word co-occurrence. This document, word and keyword representation is further used as an input feature for the next layer of short-text classification. The experimental results on multiple benchmark datasets show that our proposed model outperforms the state-of-the-art models for short-text classification in multiple attempts.