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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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Abstract:
Motivated by the expense in time and other resources to produce hand-crafted grammars, there has been increased interest in wide-coverage grammars automatically obtained from treebanks. In particular, recent years have seen a move towards acquiring deep (LFG, HPSG and CCG) 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 syntactic dependency trees. As is often the case in early pioneering work in natural language processing, English has been the focus of attention in the first efforts towards acquiring treebank-based deep-grammar resources, followed by treatments of, for example, German, Japanese, Chinese and Spanish. However, to date no comparable large-scale automatically acquired deep-grammar resources have been obtained for French. The goal of the research presented in this thesis is to develop, implement, and evaluate treebank-based deep-grammar acquisition techniques for French. Along the way towards achieving this goal, this thesis presents the derivation of a new treebank for French from the Paris 7 Treebank, the Modified French Treebank, a cleaner, more coherent treebank with several transformed structures and new linguistic analyses. Statistical parsers trained on this data outperform those trained on the original Paris 7 Treebank, which has five times the amount of data. The Modified French Treebank is the data source used for the development of treebank-based automatic deep-grammar acquisition for LFG parsing resources for French, based on an f-structure annotation algorithm for this treebank. LFG CFG-based parsing architectures are then extended and tested, achieving a competitive best f-score of 86.73% for all features. The CFG-based parsing architectures are then complemented with an alternative dependency-based statistical parsing approach, obviating the CFG-based parsing step, and instead directly parsing strings into f-structures.
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Keyword:
Computational linguistics; Treebank-Based Deep LFG Grammar Acquisition
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URL: http://doras.dcu.ie/16077/
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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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