SPUCL (Scientific Publication Classifier): A Human-Readable Labelling System for Scientific Publications
Source abstract
Abstract
To assess critically the scientific literature is a very challenging task; in general it requires analysing a lot of documents to define the state-of-the-art of a research field and classifying them. The documents classifier systems have tried to address this problem by different techniques such as probabilistic, machine learning and neural networks models. One of the most popular document classification approaches is the LDA (Latent Dirichlet Allocation), a probabilistic topic model. One of the main issues of the LDA approach is that the retrieved topics are a collection of terms with their probabilities and it does not have a human-readable form. This paper defines an approach to make LDA topics comprehensible for humans by the exploitation of the Word2Vec approach.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Advanced Text Analysis Techniques · Biomedical Text Mining and Ontologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Departement of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, 00166 Rome, Italy
Agency for Digital Italy (AgID), Smart Cities Service Via Liszt 21, 00144 Rome, Italy
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