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1
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018)
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2
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ...
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3
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ...
Litschko, Robert; Glavas, Goran; Ponzetto, Simone Paolo. - : Apollo - University of Cambridge Repository, 2018
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4
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
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5
On the Limitations of Unsupervised Bilingual Dictionary Induction ...
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6
Fully Statistical Neural Belief Tracking ...
Mrkšić, Nikola; Vulić, Ivan. - : arXiv, 2018
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7
Bio-SimVerb ...
Chiu, Hon Wing; Pyysalo, Sampo; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2018
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8
Scoring Lexical Entailment with a Supervised Directional Similarity Network ...
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9
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ...
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10
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
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11
Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP ...
Ponti, Edoardo; Reichart, Roi; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2018
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12
Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ...
Gerz, Daniela; Vulić, Ivan; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2018
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13
A deep learning approach to bilingual lexicon induction in the biomedical domain ...
Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Apollo - University of Cambridge Repository, 2018
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14
Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation ...
Vulic, Ivan; Korhonen, Anna-Leena; Linguist, Assoc Computat. - : Apollo - University of Cambridge Repository, 2018
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15
Investigating the cross-lingual translatability of VerbNet-style classification. ...
Abstract: VerbNet-the most extensive online verb lexicon currently available for English-has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, ...
URL: https://dx.doi.org/10.17863/cam.23410
https://www.repository.cam.ac.uk/handle/1810/276130
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16
Specialising Word Vectors for Lexical Entailment ...
Vulic, Ivan; Mrk�I?, Nikola. - : Apollo - University of Cambridge Repository, 2018
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17
A deep learning approach to bilingual lexicon induction in the biomedical domain. ...
Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Apollo - University of Cambridge Repository, 2018
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18
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ...
Vulic, Ivan; Glavaš, Goran; Mrkšić, Nikola. - : Apollo - University of Cambridge Repository, 2018
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19
A deep learning approach to bilingual lexicon induction in the biomedical domain.
Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Springer Science and Business Media LLC, 2018. : BMC Bioinformatics, 2018
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20
Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
Gerz, Daniela; Vulić, Ivan; Ponti, Edoardo. - : MIT Press - Journals, 2018. : Transactions of the Association for Computational Linguistics, 2018
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