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Hits 81 – 100 of 112

81
Post-specialisation: Retrofitting vectors of words unseen in lexical resources
Mrkšić, Nikola; Glavaš, Goran; Korhonen, Anna. - : Association for Computational Linguistics, 2018
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82
Discriminating between lexico-semantic relations with the specialization tensor model
Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018
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83
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
Ponti, Edoardo Maria; Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018
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84
Explicit retrofitting of distributional word vectors
Glavaš, Goran; Vulić, Ivan. - : Association for Computational Linguistics, 2018
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85
A resource-light method for cross-lingual semantic textual similarity
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86
Cross-lingual classification of topics in political texts
Glavaš, Goran [Verfasser]; Nanni, Federico [Verfasser]; Ponzetto, Simone Paolo [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2017
DNB Subject Category Language
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87
Improving neural knowledge base completion with cross-lingual projections
Klein, Patrick [Verfasser]; Ponzetto, Simone Paolo [Verfasser]; Glavaš, Goran [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2017
DNB Subject Category Language
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88
Dual tensor model for detecting asymmetric lexico-semantic relations
Glavaš, Goran [Verfasser]; Ponzetto, Simone Paolo [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2017
DNB Subject Category Language
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89
Unsupervised cross-lingual scaling of political texts
Nanni, Federico; Ponzetto, Simone Paolo; Glavaš, Goran. - : Association for Computational Linguistics, 2017
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90
University of Mannheim @ CLSciSumm-17: Citation-Based Summarization of Scientific Articles Using Semantic Textual Similarity
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91
Cross-lingual classification of topics in political texts
Ponzetto, Simone Paolo; Nanni, Federico; Glavaš, Goran. - : Association for Computational Linguistics (ACL), 2017
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92
Improving neural knowledge base completion with cross-lingual projections
Klein, Patrick; Glavaš, Goran; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2017
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93
Leveraging event-based semantics for automated text simplification
Štajner, Sanja; Glavaš, Goran. - : Elsevier, 2017
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94
Two layers of annotation for representing event mentions in news stories
Buono, Maria Pia di; Tutek, Martin; Šnajder, Jan. - : Association for Computational Linguistics, 2017
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95
If sentences could see: Investigating visual information for semantic textual similarity
Glavaš, Goran; Vulić, Ivan; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2017
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96
Dual tensor model for detecting asymmetric lexico-semantic relations
Glavaš, Goran; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2017
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97
Predicting news values from headline text and emotions
Buono, Maria Pia di; Šnajder, Jan; Dalbelo Bašić, Bojana. - : Association for Computational Linguistics, 2017
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98
Unsupervised text segmentation using semantic relatedness graphs
Glavaš, Goran; Nanni, Federico; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2016
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99
Spanish NER with word representations and conditional random fields
Copara Zea, Jenny Linet; Ochoa Luna, José Eduardo; Thorne, Camilo. - : Association for Computational Linguistics, 2016
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100
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
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