Partially supervised coreference resolution for opinion summarization through structured rule learning

This work proposes and evaluates a new algorithm for the task of source coreference resolution that outperforms competitive baselines and approaches the problem as the novel problem of partially supervised clustering.

Abstract

Combining fine-grained opinion information to produce opinion summaries is important for sentiment analysis applications. Toward that end, we tackle the problem of source coreference resolution -- linking together source mentions that refer to the same entity. The partially supervised nature of the problem leads us to define and approach it as the novel problem of partially supervised clustering. We propose and evaluate a new algorithm for the task of source coreference resolution that outperforms competitive baselines.