DE eng

Search in the Catalogues and Directories

Page: 1 2 3
Hits 21 – 40 of 52

21
A Non-Linear Structural Probe
In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
BASE
Show details
22
Disambiguatory Signals are Stronger in Word-initial Positions
In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
BASE
Show details
23
How (Non-)Optimal is the Lexicon?
In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
BASE
Show details
24
A Bayesian Framework for Information-Theoretic Probing
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
BASE
Show details
25
How (Non-)Optimal is the Lexicon? ...
NAACL 2021 2021; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021
BASE
Show details
26
A Non-Linear Structural Probe ...
BASE
Show details
27
How (Non-)Optimal is the Lexicon? ...
BASE
Show details
28
Disambiguatory Signals are Stronger in Word-initial Positions ...
Pimentel, Tiago; Cotterell, Ryan; Roark, Brian. - : ETH Zurich, 2021
BASE
Show details
29
Finding Concept-specific Biases in Form--Meaning Associations ...
Abstract: Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.349/ Abstract: This work presents an information-theoretic operationalisation of cross-linguistic non-arbitrariness. It is not a new idea that there are small, cross-linguistic associations between the forms and meanings of words. For instance, it has been claimed (Blasi et al., 2016) that the word for "tongue" is more likely than chance to contain the phone [l]. By controlling for the influence of language family and geographic proximity within a very large concept-aligned, cross-lingual lexicon, we extend methods previously used to detect within language non-arbitrariness (Pimentel et al., 2019) to measure cross-linguistic associations. We find that there is a significant effect of non-arbitrariness, but it is unsurprisingly small (less than 0.5% on average according to our information-theoretic estimate). We also provide a concept-level analysis which shows that a quarter of the concepts considered in our work exhibit a ...
Keyword: Artificial Intelligence; Computer Science and Engineering; Intelligent System; Natural Language Processing; Psycholinguistics
URL: https://underline.io/lecture/19655-finding-concept-specific-biases-in-form--meaning-associations
https://dx.doi.org/10.48448/gkde-dn79
BASE
Hide details
30
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
BASE
Show details
31
Information-Theoretic Probing for Linguistic Structure ...
BASE
Show details
32
Information-Theoretic Probing for Linguistic Structure ...
BASE
Show details
33
A Corpus for Large-Scale Phonetic Typology ...
BASE
Show details
34
Phonotactic Complexity and its Trade-offs ...
BASE
Show details
35
Phonotactic Complexity and Its Trade-offs ...
Pimentel, Tiago; Roark, Brian; Cotterell, Ryan. - : ETH Zurich, 2020
BASE
Show details
36
A Corpus for Large-Scale Phonetic Typology ...
BASE
Show details
37
Predicting Declension Class from Form and Meaning ...
BASE
Show details
38
A corpus for large-scale phonetic typology
BASE
Show details
39
Predicting declension class from form and meaning
BASE
Show details
40
Pareto Probing: Trading Off Accuracy for Complexity
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
BASE
Show details

Page: 1 2 3

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
52
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern