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- W3103371880 abstract "We introduce and study semantic capacity of terms. For example, the semantic capacity of artificial intelligence is higher than that of linear regression since artificial intelligence possesses a broader meaning scope. Understanding semantic capacity of terms will help many downstream tasks in natural language processing. For this purpose, we propose a two-step model to investigate semantic capacity of terms, which takes a large text corpus as input and can evaluate semantic capacity of terms if the text corpus can provide enough co-occurrence information of terms. Extensive experiments in three fields demonstrate the effectiveness and rationality of our model compared with well-designed baselines and human-level evaluations." @default.
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- W3103371880 date "2020-01-01" @default.
- W3103371880 modified "2023-09-24" @default.
- W3103371880 title "Exploring Semantic Capacity of Terms" @default.
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- W3103371880 doi "https://doi.org/10.18653/v1/2020.emnlp-main.684" @default.
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