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1
Semantic changes in harm-related concepts in English ...
Vylomova, Ekaterina; Haslam, Nick. - : Zenodo, 2021
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2
Semantic changes in harm-related concepts in English ...
Vylomova, Ekaterina; Haslam, Nick. - : Zenodo, 2021
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3
More confident, less formal: stylistic changes in academic psychology writing from 1970 to 2016
In: Scientometrics, Vol. 126, no. 12 (Dec 2021), pp. 9603-9612 (2021)
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4
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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5
SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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6
UniMorph 3.0: Universal Morphology
In: Proceedings of the 12th Language Resources and Evaluation Conference (2020)
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7
UniMorph 3.0: Universal Morphology ...
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8
Harm inflation: Making sense of concept creep
In: European Review of Social Psychology, Vol. 31, no. 1 (Jan 2020), pp. 254-286 (2020)
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9
Contextualization of Morphological Inflection ...
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10
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ...
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11
Compositional morphology through deep learning
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12
Context-Aware Prediction of Derivational Word-forms ...
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13
Paradigm Completion for Derivational Morphology ...
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14
Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning ...
Abstract: Recent work on word embeddings has shown that simple vector subtraction over pre-trained embeddings is surprisingly effective at capturing different lexical relations, despite lacking explicit supervision. Prior work has evaluated this intriguing result using a word analogy prediction formulation and hand-selected relations, but the generality of the finding over a broader range of lexical relation types and different learning settings has not been evaluated. In this paper, we carry out such an evaluation in two learning settings: (1) spectral clustering to induce word relations, and (2) supervised learning to classify vector differences into relation types. We find that word embeddings capture a surprising amount of information, and that, under suitable supervised training, vector subtraction generalises well to a broad range of relations, including over unseen lexical items. ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1509.01692
https://dx.doi.org/10.48550/arxiv.1509.01692
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