CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics
| Autor: | Enrico Motta, Francesco Osborne, Angelo Antonio Salatino |
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| Přispěvatelé: | Salatino, A, Osborne, F, Motta, E |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Information retrieval
Text mining Ontology Computer science INF/01 - INFORMATICA 02 engineering and technology Library and Information Sciences Ontology (information science) Digital library Field (computer science) Set (abstract data type) Digital librarie 020204 information systems Taxonomy (general) Component (UML) Scalability Scholarly data Science of science 0202 electrical engineering electronic engineering information engineering ING-INF/01 - ELETTRONICA 020201 artificial intelligence & image processing Topic detection Classifier (UML) |
| ISSN: | 1432-1300 |
| Popis: | Classifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields. |
| Databáze: | OpenAIRE |
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