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What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification ...
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Cross-Lingual Cross-Domain Nested Named Entity Evaluation on English Web Texts ...
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Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
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When is multitask learning effective? Semantic sequence prediction under varying data conditions
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In: EACL 2017 - 15th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.inria.fr/hal-01677427 ; EACL 2017 - 15th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2017, Valencia, Spain. pp.1-10 ; http://eacl2017.org/ (2017)
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Parsing Universal Dependencies without training
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In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, ; EACL 2017 - 15th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.inria.fr/hal-01677405 ; EACL 2017 - 15th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2017, Valencia, Spain. pp.229 - 239 ; http://eacl2017.org/ (2017)
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Abstract:
International audience ; We propose UDP, the first training-free parser for Universal Dependencies (UD). Our algorithm is based on PageRank and a small set of head attachment rules. It features two-step decoding to guarantee that function words are attached as leaf nodes. The parser requires no training, and it is competitive with a delexicalized transfer system. UDP offers a linguistically sound unsupervised alternative to cross-lingual parsing for UD, which can be used as a baseline for such systems. The parser has very few parameters and is distinctly robust to domain change across languages.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
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URL: https://hal.inria.fr/hal-01677405 https://hal.inria.fr/hal-01677405/file/alonso2017-parsing.pdf https://hal.inria.fr/hal-01677405/document
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Universal Dependencies 2.0 – CoNLL 2017 Shared Task Development and Test Data
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