Annals of Computer Science and Information Systems, Volume 12

DOI: http://dx.doi.org/10.15439/2017F531

Citation: Position Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 12, pages 71–78 (2017)

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Abstract. In this position paper, we put forward two claims: 1) it is possible to design a dynamic and extensible corpus without running the risk of getting into scalability problems; 2) it is possible to devise noise-resistant Language Technology applications without affecting performance. To support our claims, we describe the design, construction and limitations of a very specialized medical web corpus, called eCare\_Sv\_01, and we present two experiments on lay-specialized text classification. eCare\_Sv\_01 is a small corpus of web documents written in Swedish. The corpus contains documents about chronic diseases. The sublanguage used in each document has been labelled as``lay'' or``specialized'' by a lay annotator. The corpus is designed as a flexible text resource, where additional medical documents will be appended over time. Experiments show that the lay-specialized labels assigned by the lay annotator are reliably learned by standard classifiers. More specifically, Experiment 1 shows that scalability is not an issue when increasing the size of the datasets to be learned from 156 up to 801 documents. Experiment 2 shows that lay-specialized labels can be learned regardless of the large amount of disturbing factors, such as machine translated documents or low-quality texts that are numerous in the corpus.

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