Reducing Over-generation Errors for Automatic Keyphrase Extraction using Integer Linear Programming

摘要

We introduce a global inference model for keyphrase extraction that reduces over-generation errors by weighting sets of keyphrase candidates according to their component words. Our model can be applied on top of any supervised or unsuper-vised word weighting function. Experimental results show a substantial improvement over commonly used word-based ranking approaches.