GOW-LDA: Applying Term Co-occurrence Graph Representation in LDA Topic Models Improvement
| Autor: | Phu Pham, Phuc Do, Chien D.C. Ta |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Topic model
business.industry Computer science Probabilistic logic Co-occurrence Pattern recognition 02 engineering and technology Graph Statistical classification ComputingMethodologies_PATTERNRECOGNITION 020204 information systems 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | Lecture Notes in Electrical Engineering ISBN: 9789811082757 |
| DOI: | 10.1007/978-981-10-8276-4_40 |
| Popis: | In this paper, we demonstrate a novel approach in topic model exploration by applying word co-occurrence graph or g raph-o f-w ords (GOW) in order to produce more informative extracted latent topics from a large document corpus. According to the L atent D irichlet A llocation (LDA) algorithm, it only considers the words occurrence independently via probabilistic distributions. It leads to the failure in term’s relationship recognition. Hence in order to overcome this disadvantage of traditional LDA, we propose a novel approach, called GOW-LDA. The GOW-LDA is proposed that combines the GOW graph used in document representation, the frequent subgraph extracting and distribution model of LDA. For evaluation, we compare our proposed model with the traditional one in different classification algorithms. The comparative evaluation is performed in this study by using the standardized datasets. The results generated by the experiments show that the proposed algorithm yields performance respectably. |
| Databáze: | OpenAIRE |
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