Interaction Matching for Long-Tail Multi-Label Classification
- URL: http://arxiv.org/abs/2005.08805v1
- Date: Mon, 18 May 2020 15:27:55 GMT
- Title: Interaction Matching for Long-Tail Multi-Label Classification
- Authors: Sean MacAvaney, Franck Dernoncourt, Walter Chang, Nazli Goharian,
Ophir Frieder
- Abstract summary: We present an elegant and effective approach for addressing limitations in existing multi-label classification models.
By performing soft n-gram interaction matching, we match labels with natural language descriptions.
- Score: 57.262792333593644
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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).
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