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1
Finding Concept-specific Biases in Form--Meaning Associations ...
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2
Systematic Inequalities in Language Technology Performance across the World's Languages ...
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3
Modeling the Unigram Distribution ...
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4
A surprisal--duration trade-off across and within the world's languages ...
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5
Modeling the Unigram Distribution ...
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6
Finding Concept-specific Biases in Form–Meaning Associations ...
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7
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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8
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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9
How (Non-)Optimal is the Lexicon? ...
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10
Evaluating Word Embeddings with Categorical Modularity ...
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11
Modeling the Unigram Distribution
In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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12
Finding Concept-specific Biases in Form–Meaning Associations
In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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13
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)
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14
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs
In: Transactions of the Association for Computational Linguistics, 9 (2021)
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15
Evaluating Word Embeddings with Categorical Modularity ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.174 Abstract: We introduce categorical modularity, a novel low-resource intrinsic metric to evaluate word embedding quality. Categorical modularity is a graph modularity metric based on the k-nearest neighbor graph constructed with embedding vectors of words from a fixed set of semantic categories, in which the goal is to measure the proportion of words that have nearest neighbors within the same categories. We use a core set of 500 words belonging to 59 neurobiologically motivated semantic categories in 29 languages and analyze three word embedding models per language (FastText, MUSE, and subs2vec). We find moderate to strong positive correlations between categorical modularity and performance on the monolingual tasks of sentiment analysis and word similarity calculation and on the cross-lingual task of bilingual lexicon induction both to and from English. Overall, we suggest that categorical modularity provides non-trivial predictive information ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/26265-evaluating-word-embeddings-with-categorical-modularity
https://dx.doi.org/10.48448/x7g0-rv80
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16
How (Non-)Optimal is the Lexicon? ...
NAACL 2021 2021; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021
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17
How (Non-)Optimal is the Lexicon? ...
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18
Finding Concept-specific Biases in Form--Meaning Associations ...
NAACL 2021 2021; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021
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19
Neural signatures of syntactic variation in speech planning
In: PLoS Biol (2021)
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20
Meaning before grammar: A review of ERP experiments on the neurodevelopmental origins of semantic processing [<Journal>]
Morgan, Elena Usai [Verfasser]; Meer, Audrey van der [Verfasser]; Vulchanova, Mila [Verfasser].
DNB Subject Category Language
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