In this paper, a novel corpus based learning approach to noun phrase coreference resolution is presented This approach aims to solve not only pronoun anaphora problem, but also a more general noun phrase coreference one, which is introduced by MUC By applying the maximum entropy (M E ) model and utilizing a flexible object based architecture, the system is able to make use of a range of knowledge sources in training the classifier and achieves an F measure of 60 2%, which is very close to the state of art result (61 8%), on the MUC 7 coreference resolution task corpus

The maximum entropy model and flexible object-based architecture effectively resolve noun phrase coreference in English, with an F measure of 60 2% on the MUC 7 coreference resolution task corpus.

English Noun Phrase Coreference Resolution via a Maximum Entropy Model

Key Takeaway: The maximum entropy model and flexible object-based architecture effectively resolve noun phrase coreference in English, with an F measure of 60 2% on the MUC 7 coreference resolution task corpus.