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Come hither or go away? Recognising pre-electoral coalition signals in the news ...
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Come hither or go away? Recognising pre-electoral coalition signals in the news
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Treebanking user-generated content: a proposal for a unified representation in universal dependencies
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In: Sanguinetti, Manuela orcid:0000-0002-0147-2208 , Bosco, Cristina, Cassidy, Lauren, Çetinoglu, Özlem, Cignarella, Alessandra Teresa orcid:0000-0002-4409-6679 , Lynn, Teresa, Rehbein, Ines, Ruppenhofer, Josef, Seddah, Djamé and Zeldes, Amir orcid:0000-0001-8016-6753 (2020) Treebanking user-generated content: a proposal for a unified representation in universal dependencies. In: 12th Language Resources and Evaluation Conference. (LREC 2020), 11-16 May 2020, Marseille, France. (2020)
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Treebanking user-generated content: a proposal for a unified representation in universal dependencies
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In: Sanguinetti, Manuela orcid:0000-0002-0147-2208 , Bosco, Cristina, Cassidy, Lauren, Çetinoglu, Özlem, Cignarella, Alessandra Teresa orcid:0000-0002-4409-6679 , Lynn, Teresa, Rehbein, Ines, Ruppenhofer, Josef, Seddah, Djamé and Zeldes, Amir orcid:0000-0001-8016-6753 (2020) Treebanking user-generated content: a proposal for a unified representation in universal dependencies. In: 12th Language Resources and Evaluation Conference. (LREC 2020), 11-16 May 2020, Marseille, France. (Virtual). (2020)
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I got a construction looks funny - Representing and recovering non-standard constructions in UD ...
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Treebanking User-Generated Content: a UD Based Overview of Guidelines, Corpora and Unified Recommendations ...
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German causal language annotations and lexicon (verbs, nouns, prepositions) (DE) ...
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Treebanking user-generated content: A proposal for a unified representation in universal dependencies
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Neural reranking for dependency parsing: An evaluation
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Abstract:
Recent work has shown that neural rerankers can improve results for dependency parsing over the top k trees produced by a base parser. However, all neural rerankers so far have been evaluated on English and Chinese only, both languages with a configurational word order and poor morphology. In the paper, we re-assess the potential of successful neural reranking models from the literature on English and on two morphologically rich(er) languages, German and Czech. In addition, we introduce a new variation of a discriminative reranker based on graph convolutional networks (GCNs). We show that the GCN not only outperforms previous models on English but is the only model that is able to improve results over the baselines on German and Czech. We explain the differences in reranking performance based on an analysis of a) the gold tree ratio and b) the variety in the k-best lists.
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Keyword:
004 Informatik
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URL: https://madoc.bib.uni-mannheim.de/55426 https://madoc.bib.uni-mannheim.de/55426/ https://madoc.bib.uni-mannheim.de/55426/1/2020.acl-main.379-1.pdf
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I’ve got a construction looks funny – representing and recovering non-standard constructions in UD
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