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- W2912486864 abstract "Quantifying and predicting morphological productivity is a long-standing challenge in corpus linguistics and psycholinguistics. The same challenge reappears in natural language processing in the context of handling words that were not seen in the training set (out-of-vocabulary, or OOV, words). Prior research showed that a good indicator of the productivity of a morpheme is the number of words involving it that occur exactly once (the hapax legomena). A technical connection was adduced between this result and Good-Turing smoothing, which assigns probability mass to unseen events on the basis of the simplifying assumption that word frequencies are stationary. In a large-scale study of 133 affixes in Wikipedia, we develop evidence that success in fact depends on tapping the frequency range in which the assumptions of Good-Turing are violated." @default.
- W2912486864 created "2019-02-21" @default.
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- W2912486864 date "2018-01-01" @default.
- W2912486864 modified "2023-09-23" @default.
- W2912486864 title "On Hapax Legomena and Morphological Productivity" @default.
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- W2912486864 doi "https://doi.org/10.18653/v1/w18-5814" @default.
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