: Vocabulario de PLN: score

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score

Unlike majority of the previous work we create a framework for training large number of features on human rankings and at the same time as a result of tuning produce a score based metric which does not require two (or more) hypotheses for comparison. (Stanojević, Miloš and Sima’an, Khalil, BEER: BEtter Evaluation as Ranking, Proceedings of the Ninth Workshop on Statistical Machine Translation, Association for Computational Linguistics (https://www.aclweb.org/anthology/W14-3354 ), 2014)

M m=1 whose scores are {s train } (Xie, Cai, Kong, Zhou & Qu, 2022)
The score reflects how important a word is to a document in a corpus. (Kumar, Sethi, Akhtar, Ekbal, Biemann & Bhattacharyya, 2017)

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