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Interaction Matching for Long-Tail Multi-Label Classification

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Authors: Sean MacAvaney, Franck Dernoncourt, Walter Chang, Nazli Goharian, Ophir Frieder

Appeared in: arXiv

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).

BibTeX @article{macavaney:arxiv2020-extr, author = {MacAvaney, Sean and Dernoncourt, Franck and Chang, Walter and Goharian, Nazli and Frieder, Ophir}, title = {Interaction Matching for Long-Tail Multi-Label Classification}, year = {2020}, url = {https://arxiv.org/abs/2005.08805}, journal = {arXiv}, volume = {abs/2005.08805} }