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81
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
Ponti, Edoardo; O'Horan, Helen; Berzak, Yevgeni. - : Apollo - University of Cambridge Repository, 2019
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82
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ...
Shareghi, Ehsan; Gerz, Daniela; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2019
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83
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions ...
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84
Specialising Distributional Vectors of All Words for Lexical Entailment ...
Kamath, Aishwarya; Pfeiffer, Jonas; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2019
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85
Multilingual and cross-lingual graded lexical entailment ...
Vulic, Ivan; Ponzetto, SP; Glavaš, G. - : Apollo - University of Cambridge Repository, 2019
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86
Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment ...
Glavaš, G; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2019
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87
Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment
Glavaš, G; Vulic, Ivan. - : Association for Computational Linguistics, 2019. : ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2019
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88
Multilingual and cross-lingual graded lexical entailment
Vulic, Ivan; Ponzetto, SP; Glavaš, G. - : Association for Computational Linguistics, 2019. : ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2019
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89
How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
Glavaš, G; Litschko, R; Ruder, S. - : Association for Computational Linguistics, 2019. : ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2019
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90
JW300: A wide-coverage parallel corpus for low-resource languages
Agic, Ž; Vulic, Ivan. - : Association for Computational Linguistics, 2019. : ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2019
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91
Unsupervised cross-lingual representation learning
Ruder, S; Søgaard, A; Vulic, Ivan. - : ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Tutorial Abstracts, 2019
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92
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Reichart, Roi; Shutova, Ekaterina; Korhonen, Anna-Leena. - : MIT Press - Journals, 2019. : COMPUTATIONAL LINGUISTICS, 2019
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93
Multilingual and cross-lingual graded lexical entailment
Glavaš, Goran; Vulić, Ivan; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2019
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94
Specializing distributional vectors of all words for lexical entailment
Ponti, Edoardo Maria; Kamath, Aishwarya; Pfeiffer, Jonas. - : Association for Computational Linguistics, 2019
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95
How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
Glavaš, Goran; Litschko, Robert; Ruder, Sebastian. - : Association for Computational Linguistics, 2019
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96
Cross-lingual semantic specialization via lexical relation induction
Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2019
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97
Generalized tuning of distributional word vectors for monolingual and cross-lingual lexical entailment
Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2019
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98
Do we really need fully unsupervised cross-lingual embeddings?
Vulić, Ivan; Glavaš, Goran; Reichart, Roi. - : Association for Computational Linguistics, 2019
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99
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
In: Computational Linguistics, Vol 45, Iss 3, Pp 559-601 (2019) (2019)
Abstract: Linguistic typology aims to capture structural and semantic variation across the world’s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge.
Keyword: Computational linguistics. Natural language processing; P98-98.5
URL: https://doaj.org/article/e766c2c989b842e388991cd857e2c997
https://doi.org/10.1162/coli_a_00357
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100
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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