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- W3025957788 abstract "We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be useful for ad-hoc search result ranking. By performing soft n-gram interaction matching, we match labels with natural language descriptions (which are common to have in most multi-labeling tasks). Our approach can be used to enhance existing multi-label classification approaches, which are biased toward frequently-occurring labels. We evaluate our approach on two challenging tasks: automatic medical coding of clinical notes and automatic labeling of entities from software tutorial text. Our results show that our method can yield up to an 11% relative improvement in macro performance, with most of the gains stemming labels that appear infrequently in the training set (i.e., the long tail of labels)." @default.
- W3025957788 created "2020-05-21" @default.
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- W3025957788 date "2020-05-18" @default.
- W3025957788 modified "2023-09-27" @default.
- W3025957788 title "Interaction Matching for Long-Tail Multi-Label Classification" @default.
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