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    • Glavaš, Goran (3)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Unsupervised text segmentation using semantic relatedness graphs Glavaš, Goran; Nanni, Federico; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2016 BASE Show details 2 Spanish NER with word representations and conditional random fields Copara Zea, Jenny Linet; Ochoa Luna, José Eduardo; Thorne, Camilo. - : Association for Computational Linguistics, 2016 BASE Show details 3 Capturing interdisciplinarity in academic abstracts Nanni, Federico; Dietz, Laura; Faralli, Stefano; Glavaš, Goran; Ponzetto, Simone Paolo. - : Corporation for National Research Initiatives, 2016 Abstract: In this work we investigate the effectiveness of different text mining methods for the task of automated identification of interdisciplinary doctoral dissertations, considering solely the content of their abstracts. In contrast to previous attempts, we frame the interdisciplinarity detection as a two step classification process: we first predict the main discipline of the dissertation using a supervised multi-class classifier and then exploit the distribution of prediction confidences of the first classifier as input for the binary classification of interdisciplinarity. For both supervised classification models we experiment with several different sets of features ranging from standard lexical features such as TF-IDF weighted vectors over topic modelling distributions to latent semantic textual representations known as word embeddings. In contrast to previous findings, our experimental results suggest that interdisciplinarity is better detected when directly using textual features than when inferring from the results of main discipline classification. Keyword: 004 Informatik; 020 Bibliotheks- und Informationswissenschaft URL: https://doi.org/10.1045/september2016-nanni https://madoc.bib.uni-mannheim.de/41256/ http://www.dlib.org/dlib/september16/nanni/09nanni.print.html BASE Hide details

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