The model adopts the Inductive Logic Programming ( ILP ) algorithm, which provides a relational way to organize different knowledge of entities and mentions.

The evaluation on the ACE data set shows that the ILP based entity-mention model is effective for the coreference resolution task.

This requirement motivates our use of Inductive Logic Programming ( ILP ), a learning algorithm capable of inferring logic programs.

The relational nature of ILP makes it possible to explicitly represent relations between an entity and its mentions, and thus provides a powerful expressiveness for the coreference resolution task.

ILP uses logic programming as a uniform representation for examples, background knowledge and hypotheses.

The model employs Inductive Logic Programming ( ILP ) to represent the relational knowledge of an active mention, an entity, and the mentions in the entity.

In the next section, we will present a more expressive entity-mention model by using ILP .

Inductive Logic Programming ( ILP ) has been applied to some natural language processing tasks, including parsing (Mooney, 1997), POS disambiguation (Cussens, 1996), lexicon construction (Claveau et al., 2003), WSD (Specia et al., 2007), and so on.