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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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SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations
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In: Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies ; https://hal.archives-ouvertes.fr/hal-02977455 ; Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Oct 2018, Bruxelles, Belgium. pp.143-152, ⟨10.18653/v1/K18-2014⟩ ; https://www.conll.org/2018/ (2018)
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Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian
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In: LREC 2018 Proceedings ; Language Resource and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-01856178 ; Language Resource and Evaluation Conference, ELRA, May 2018, Miyazaki, Japan ; http://www.lrec-conf.org/proceedings/lrec2018/pdf/600.pdf (2018)
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Dependency Parsing of Code-Switching Data with Cross-Lingual Feature Representations
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In: International Workshop on Computational Linguistics for Uralic Languages ; https://hal.archives-ouvertes.fr/hal-01722243 ; International Workshop on Computational Linguistics for Uralic Languages, Jan 2018, Helsinki, Finland. pp.1 - 17 ; aclweb.org/anthology/W18-0200 (2018)
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Universal Dependencies 2.2
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In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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Language, Cognition, and Computational Models
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In: https://hal.archives-ouvertes.fr/hal-01722351 ; Cambridge University Press, 2018 ; https://www.cambridge.org/core/books/language-cognition-and-computational-models/90CC7DBA6CADB1FE361266D311CB4413 (2018)
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Introduction: Cognitive Issues in Natural Language Processing
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In: Language, Cognition, and Computational Models ; https://hal.archives-ouvertes.fr/hal-01722353 ; Language, Cognition, and Computational Models, Cambridge University Press, 2018, 9781316676974 ; https://doi.org/10.1017/9781316676974 (2018)
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| 11 |
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ...
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Dependency Parsing of Code-Switching Data with Cross-Lingual Feature Representations
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In: International Workshop on Computational Linguistics for Uralic languages. - Helsinki, ISBN: (2018)
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| 13 |
Rating Distributions and Bayesian Inference. Enhancing Cognitive Models of Spatial Language Use
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Abstract:
Kluth T, Schultheis H. Rating Distributions and Bayesian Inference. Enhancing Cognitive Models of Spatial Language Use. In: Idiart M, Lenci A, Poibeau T, Villavicencio A, eds. Proceedings of the Eighth Workshop on Cognitive Aspects of Computational Language Learning and Processing . Melbourne, Australia: Association for Computational Linguistics; 2018: 47-55. ; We present two methods that improve the assessment of cognitive models. The first method is applicable to models computing average acceptability ratings. For these models, we propose an extension that simulates a full rating distribution (instead of average ratings) and allows generating individual ratings. Our second method enables Bayesian inference for models generating individual data. To this end, we propose to use the cross-match test (Rosenbaum, 2005) as a likelihood function. We exemplarily present both methods using cognitive models from the domain of spatial language use. For spatial language use, determining linguistic acceptability judgments of a spatial preposition for a depicted spatial relation is assumed to be a crucial process (Logan and Sadler, 1996). Existing models of this process compute an average acceptability rating. We extend the models and – based on existing data – show that the extended models allow extracting more information from the empirical data and yield more readily interpretable information about model successes and failures. Applying Bayesian inference, we find that model performance relies less on mechanisms of capturing geometrical aspects than on mapping the captured geometry to a rating interval.
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Keyword:
Bayesian inference; cognitive modeling; ddc:000; ddc:410; linguistic judgments; spatial language
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URL: https://pub.uni-bielefeld.de/record/2920199
https://pub.uni-bielefeld.de/download/2920199/2920200
https://nbn-resolving.org/urn:nbn:de:0070-pub-29201995
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