C3EL: A Joint Model for Cross-Document Co-Reference Resolution and Entity Linking

Cross-document co-reference resolution (CCR) computes equivalence classes over textual mentions denoting the same entity in a document corpus. Named-entity linking (NEL) disambiguates mentions onto entities present in a knowledge base (KB) or maps them to null if not present in the KB. Traditionally, CCR and NEL have been addressed separately. However, such approaches miss out on the mutual synergies if CCR and NEL were performed jointly. This paper proposes C3EL, an unsupervised framework combining CCR and NEL for jointly tackling both problems. C3EL incorporates results from the CCR stage into NEL, and vice versa: additional global context obtained from CCR improves the feature space and performance of NEL, while NEL in turn provides distant KB features for already disambiguated mentions to improve CCR. The CCR and NEL steps are interleaved in an iterative algorithm that focuses on the highest-confidence still unresolved mentions in each iteration. Experimental results on two different corpora, news-centric and web-centric, demonstrate significant gains over state-of-the-art baselines for both CCR and NEL.