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MassiveSumm: a very large-scale, very multilingual, news summarisation dataset ...
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IAPUCP at SemEval-2021 task 1: Stacking fine-tuned transformers is almost all you need for lexical complexity prediction
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Predicting Declension Class from Form and Meaning
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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The Paradigm Discovery Problem
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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A Tale of a Probe and a Parser
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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A Corpus for Large-Scale Phonetic Typology
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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Information-Theoretic Probing for Linguistic Structure
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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ASSET: A dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations
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Non-linear instance-based cross-lingual mapping for non-isomorphic embedding spaces
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Classification-based self-learning for weakly supervised bilingual lexicon induction
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On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation
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Multilingual Projection for Parsing Truly Low-Resource Languageš
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-01426754 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2016 (2016)
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Treebank-Based Deep Grammar Acquisition for French Probabilistic Parsing Resources
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Schluter, Natalie. - : Dublin City University. National Centre for Language Technology (NCLT), 2011. : Dublin City University. School of Computing, 2011
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In: Schluter, Natalie (2011) Treebank-Based Deep Grammar Acquisition for French Probabilistic Parsing Resources. PhD thesis, Dublin City University. (2011)
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Dependency parsing resources for French: Converting acquired lexical functional grammar F-Structure annotations and parsing F-Structures directly
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In: Schluter, Natalie and van Genabith, Josef orcid:0000-0003-1322-7944 (2009) Dependency parsing resources for French: Converting acquired lexical functional grammar F-Structure annotations and parsing F-Structures directly. In: Nodalida 2009 Conference, 14 - 16 May 2009, Odense, Denmark. (2009)
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Treebank-based acquisition of LFG parsing resources for French
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In: Schluter, Natalie and van Genabith, Josef (2008) Treebank-based acquisition of LFG parsing resources for French. In: the Sixth International Language Resources and Evaluation Conference (LREC'08), May 28-30, 2008, Marrakech, Morocco. (2008)
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Abstract:
Motivated by the expense in time and other resources to produce hand-crafted grammars, there has been increased interest in automatically obtained wide-coverage grammars from treebanks for natural language processing. In particular, recent years have seen the growth in interest in automatically obtained deep resources that can represent information absent from simple CFG-type structured treebanks and which are considered to produce more language-neutral linguistic representations, such as dependency syntactic trees. As is often the case in early pioneering work on natural language processing, English has provided the focus of first efforts towards acquiring deep-grammar resources, followed by successful treatments of, for example, German, Japanese, Chinese and Spanish. However, no comparable large-scale automatically acquired deep-grammar resources have been obtained for French to date. The goal of this paper is to present the application of treebank-based language acquisition to the case of French. We show that with modest changes to the established parsing architectures, encouraging results can be obtained for French, with a best dependency structure f-score of 86.73%.
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Keyword:
Lexical Functional Grammars; LFG; Machine translating
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URL: http://doras.dcu.ie/16176/
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