Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling
| Autor: | Hosna Omidvarborna, Mohsen Pourvali, Salvatore Orlando |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Topic model Topic structure Computer Networks and Communications Computer science Text Mining media_common.quotation_subject Cluster Labeling text mining 02 engineering and technology lcsh:Technology document clustering Text mining Artificial Intelligence Document Clustering 0202 electrical engineering electronic engineering information engineering Quality (business) Cluster analysis media_common Document Enriching cluster labeling Information retrieval Settore INF/01 - Informatica business.industry lcsh:T 05 social sciences IJIMAI 020207 software engineering document enriching Document clustering Sensor fusion Computer Science Applications Signal Processing Cluster labeling ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Computer Vision and Pattern Recognition 0509 other social sciences 050904 information & library sciences business |
| Zdroj: | International Journal of Interactive Multimedia and Artificial Intelligence, Vol 5, Iss 4, Pp 28-34 (2019) Re-Unir. Archivo Institucional de la Universidad Internacional de La Rioja instname |
| ISSN: | 1989-1660 |
| Popis: | Topic modeling algorithms are statistical methods that aim to discover the topics running through the text documents. Using topic models in machine learning and text mining is popular due to its applicability in inferring the latent topic structure of a corpus. In this paper, we represent an enriching document approach, using state-ofthe-art topic models and data fusion methods, to enrich documents of a collection with the aim of improving the quality of text clustering and cluster labeling. We propose a bi-vector space model in which every document of the corpus is represented by two vectors: one is generated based on the fusion-based topic modeling approach, and one simply is the traditional vector model. Our experiments on various datasets show that using a combination of topic modeling and fusion methods to create documents’ vectors can significantly improve the quality of the results in clustering the documents. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|