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- aggregation classification "P1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2008".
- aggregation format "application/pdf".
- aggregation hasFormat 598390.bibtex.
- aggregation hasFormat 598390.csv.
- aggregation hasFormat 598390.dc.
- aggregation hasFormat 598390.didl.
- aggregation hasFormat 598390.doc.
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- aggregation isPartOf urn:isbn:9782951740846.
- aggregation language "eng".
- aggregation publisher "European Language Resources Association (ELRA)".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Languages and Literatures".
- aggregation title "Learning-based detection of scientific terms in patient information".
- aggregation abstract "In this paper, we investigate the use of a machine-learning based approach to the specific problem of scientific term detection in patient information. Lacking lexical databases which differentiate between the scientific and popular nature of medical terms, we used local context, morphosyntactic, morphological and statistical information to design a learner which accurately detects scientific medical terms. This study is the first step towards the automatic replacement of a scientific term by its popular counterpart, which should have a beneficial effect on readability. We show a F-score of 84% for the prediction of scientific terms in an English and Dutch EPAR corpus. Since recasting the term extraction problem as a classification problem leads to a large skewedness of the resulting data set, we rebalanced the data set through the application of some simple TF-IDF-based and Log-likelihood-based filters. We show that filtering indeed has a beneficial effect on the learner’s performance. However, the results of the filtering approach combined with the learning-based approach remain below those of the learning-based approach.".
- aggregation authorList BK151525.
- aggregation endPage "591".
- aggregation startPage "585".
- aggregation aggregates 601447.
- aggregation isDescribedBy 598390.
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