A Graph Convolutional Topic Model for Short and Noisy Text Streams

• URL: http://arxiv.org/abs/2003.06112v4
• Date: Fri, 24 Dec 2021 02:26:38 GMT
• Title: A Graph Convolutional Topic Model for Short and Noisy Text Streams
• Authors: Ngo Van Linh, Tran Xuan Bach and Khoat Than
• Abstract summary: We propose a novel graph convolutional topic model (GCTM) which integrates graph convolutional networks (GCN) into a topic model. We conduct extensive experiments to evaluate our method with both a human knowledge graph (Wordnet) and a graph built from pre-trained word embeddings (Word2vec) Our method achieves significantly better performances than state-of-the-art baselines in terms of probabilistic predictive measure and topic coherence.
• Score: 2.578242050187029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning hidden topics from data streams has become absolutely necessary but posed challenging problems such as concept drift as well as short and noisy data. Using prior knowledge to enrich a topic model is one of potential solutions to cope with these challenges. Prior knowledge that is derived from human knowledge (e.g. Wordnet) or a pre-trained model (e.g. Word2vec) is very valuable and useful to help topic models work better. However, in a streaming environment where data arrives continually and infinitely, existing studies are limited to exploiting these resources effectively. Especially, a knowledge graph, that contains meaningful word relations, is ignored. In this paper, to aim at exploiting a knowledge graph effectively, we propose a novel graph convolutional topic model (GCTM) which integrates graph convolutional networks (GCN) into a topic model and a learning method which learns the networks and the topic model simultaneously for data streams. In each minibatch, our method not only can exploit an external knowledge graph but also can balance the external and old knowledge to perform well on new data. We conduct extensive experiments to evaluate our method with both a human knowledge graph (Wordnet) and a graph built from pre-trained word embeddings (Word2vec). The experimental results show that our method achieves significantly better performances than state-of-the-art baselines in terms of probabilistic predictive measure and topic coherence. In particular, our method can work well when dealing with short texts as well as concept drift. The implementation of GCTM is available at \url{https://github.com/bachtranxuan/GCTM.git}.

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