Adaptive Learning for Improving Semantic Tagging of Scientific Articles
Andrzej Janusz, Sebastian Stawicki, Hung Son Nguyen
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 27–34 (2014)
Abstract. In this paper we consider a problem of automatic labeling of textual data with concepts explicitly deﬁned in an external knowledge base. We describe our tagging system and we also present a framework for adaptive learning of associations between terms or phrases from the texts and the concepts. Those associations are then utilized by our semantic interpreter, which is based on the Explicit Semantic Analysis (ESA) method, in order to label scientiﬁc articles indexed by our SONCA platform. Apart from the description of the learning algorithm, we show a few practical application examples of our system, in which it was used for tagging scientiﬁc articles with headings from the MeSH ontology, categories from ACM Computing Classiﬁcation System and from OECD Fields of Science and Technology Classiﬁcation.