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More confident, less formal: stylistic changes in academic psychology writing from 1970 to 2016
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In: Scientometrics, Vol. 126, no. 12 (Dec 2021), pp. 9603-9612 (2021)
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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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UniMorph 3.0: Universal Morphology
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In: Proceedings of the 12th Language Resources and Evaluation Conference (2020)
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Harm inflation: Making sense of concept creep
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In: European Review of Social Psychology, Vol. 31, no. 1 (Jan 2020), pp. 254-286 (2020)
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The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ...
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Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning ...
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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. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1509.01692 https://dx.doi.org/10.48550/arxiv.1509.01692
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