Generation of Author Topic Models Using LDA
| Autor: | G. Muthu Selvi, G. S. Mahalakshmi, S. Sendhilkumar |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Topic model
Hierarchical Dirichlet process symbols.namesake Information retrieval Blueprint Computer science 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing 02 engineering and technology 021001 nanoscience & nanotechnology 0210 nano-technology Latent Dirichlet allocation |
| Zdroj: | Computational Vision and Bio Inspired Computing ISBN: 9783319717661 |
| Popis: | Copyright and ownership of research ideas is questionable as to which author the credit should be attached to. Mining author contributions has to be approached more semantically to solve this issue. Representing the research ideas using topic distributions substantiate the measuring of author contributions. Author Topic Models (ATM) are generally obtained by applying topic modeling approaches over an author’s research articles. ATMs form the blueprints of an author. Given a research paper and the blueprints of it’s’ authors, identifying the contribution of every author in the article becomes easy. This paper proposes the generation of ATMs by applying Latent Dirichlet Allocation (LDA). |
| Databáze: | OpenAIRE |
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