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Speakers Fill Lexical Semantic Gaps with Context
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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42 |
Predicting Declension Class from Form and Meaning
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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43 |
A Tale of a Probe and a Parser
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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44 |
A Corpus for Large-Scale Phonetic Typology
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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45 |
Phonotactic Complexity and Its Trade-offs
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In: Transactions of the Association for Computational Linguistics, 8 (2020)
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Abstract:
We present methods for calculating a measure of phonotactic complexity—bits per phoneme— that permits a straightforward cross-linguistic comparison. When given a word, represented as a sequence of phonemic segments such as symbols in the international phonetic alphabet, and a statistical model trained on a sample of word types from the language, we can approximately measure bits per phoneme using the negative log-probability of that word under the model. This simple measure allows us to compare the entropy across languages, giving insight into how complex a language’s phonotactics is. Using a collection of 1016 basic concept words across 106 languages, we demonstrate a very strong negative correlation of − 0.74 between bits per phoneme and the average length of words. ; ISSN:2307-387X
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URL: https://doi.org/10.3929/ethz-b-000462324 https://hdl.handle.net/20.500.11850/462324
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46 |
Information-Theoretic Probing for Linguistic Structure
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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47 |
Metaphor Detection Using Context and Concreteness
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In: Proceedings of the Second Workshop on Figurative Language Processing (2020)
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52 |
Rethinking Phonotactic Complexity
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In: Proceedings of the Society for Computation in Linguistics (2019)
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