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Enhancing biomedical word embeddings by retrofitting to verb clusters
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Chiu, B; Baker, Simon; Palmer, M. - : Association for Computational Linguistics, 2019. : https://www.aclweb.org/anthology/W19-50, 2019. : BioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task, 2019
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102 |
Bayesian learning for neural dependency parsing
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Shareghi, E; Li, Y; Zhu, Y. - : NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 2019
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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A systematic study of leveraging subword information for learning word representations
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Zhu, Y; Korhonen, Anna-Leena; Vulić, I. - : NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 2019
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How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
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107 |
A spreading activation framework for tracking conceptual complexity of texts
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Abstract:
We propose an unsupervised approach for assessing conceptual complexity of texts, based on spreading activation. Using DBpedia knowledge graph as a proxy to long-term memory, mentioned concepts become activated and trigger further activation as the text is sequentially traversed. Drawing inspiration from psycholinguistic theories of reading comprehension, we model memory processes such as semantic priming, sentence wrap-up, and forgetting. We show that our models capture various aspects of conceptual text complexity and significantly outperform current state of the art.
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Keyword:
004 Informatik
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URL: https://madoc.bib.uni-mannheim.de/51558/ https://madoc.bib.uni-mannheim.de/51558/1/hulpus2019sa.pdf https://madoc.bib.uni-mannheim.de/51558
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108 |
Cross-lingual semantic specialization via lexical relation induction
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109 |
Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment
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110 |
Informing unsupervised pretraining with external linguistic knowledge
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111 |
Do we really need fully unsupervised cross-lingual embeddings?
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: Computational Linguistics, Vol 45, Iss 3, Pp 559-601 (2019) (2019)
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
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In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
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Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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117 |
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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118 |
Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP ...
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119 |
Acquiring verb classes through bottom-up semantic verb clustering ...
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Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ...
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