| 1 |
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Unsupervised Compositionality Prediction of Nominal Compounds
|
|
|
|
In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02318196 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (1), pp.1-57. ⟨10.1162/coli_a_00341⟩ (2019)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Unsupervised Compositionality Prediction of Nominal Compounds
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks
|
|
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Rating Distributions and Bayesian Inference. Enhancing Cognitive Models of Spatial Language Use
|
|
|
|
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.
|
|
Keyword:
Bayesian inference; cognitive modeling; ddc:000; ddc:410; linguistic judgments; spatial language
|
|
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
|
|
BASE
|
|
Hide details
|
|
|
| 7 |
LexSubNC: a Dataset of Lexical Substitution for Nominal Compounds
|
|
|
|
In: Proceedings of the 12th International Conference on Computational Semantics (IWCS 2017) - Short papers ; https://hal.archives-ouvertes.fr/hal-01795956 ; Proceedings of the 12th International Conference on Computational Semantics (IWCS 2017) - Short papers, 2017, Montpellier, France (2017)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
How Naked is the Naked Truth? A Multilingual Lexicon of Nominal Compound Compositionality
|
|
|
|
In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) ; https://hal.archives-ouvertes.fr/hal-01459911 ; Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2016, Berlin, Germany. pp.156--161, ⟨10.18653/v1/P16-2026⟩ (2016)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time
|
|
|
|
In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ; https://hal.archives-ouvertes.fr/hal-01459914 ; Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016, Berlin, Germany. pp.1986--1997, ⟨10.18653/v1/P16-1187⟩ (2016)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Picking them up and Figuring them out: Verb-Particle Constructions, Noise and Idiomaticity
|
|
|
|
In: Proceedings of the Twelfth Conference on Natural Language Learning (CoNLL 2008) ; https://hal.archives-ouvertes.fr/hal-01200612 ; Proceedings of the Twelfth Conference on Natural Language Learning (CoNLL 2008), 2008, Manchester, UK, Unknown Region. pp.49--56 (2008)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
A verb learning model driven by syntactic constructions ; Um modelo de aquisição de verbos guiado por construções sintáticas
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
A verb learning model driven by syntactic constructions ; Um modelo de aquisição de verbos guiado por construções sintáticas
|
|
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Validation and Evaluation of Automatically Acquired Multiword Expressions for Grammar Engineering
|
|
|
|
In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) ; https://hal.archives-ouvertes.fr/hal-01200614 ; Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 2007, Prague, Czech Republic. pp.1034--1043 (2007)
|
|
BASE
|
|
Show details
|
|
|
|
|
