Interaction Matching for Long-Tail Multi-Label Classification

Understand

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.