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Razvan C. Bunescu

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2 papers

1 author row

ECAI Conference 2012 Conference Paper

An Adaptive Clustering Model that Integrates Expert Rules and N-gram Statistics for Coreference Resolution

Razvan C. Bunescu

We present an adaptive clustering model for coreference resolution in which the expert rules of a state of the art deterministic system are used as features over pairs of clusters. A significant advantage of the new approach is that new features can be easily added to the system. We demonstrate this advantage by incorporating semantic compatibility features for neutral pronouns computed from web n-gram statistics. Experimental results show that the combination of the new features with the expert rules in the adaptive clustering approach results in an overall performance improvement and a substantial 5% improvement in F1 measure for the target pronouns.

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ICML Conference 2007 Conference Paper

Multiple instance learning for sparse positive bags

Razvan C. Bunescu
Raymond Mooney

We present a new approach to multiple instance learning (MIL) that is particularly effective when the positive bags are sparse (i.e. contain few positive instances). Unlike other SVM-based MIL methods, our approach more directly enforces the desired constraint that at least one of the instances in a positive bag is positive. Using both artificial and real-world data, we experimentally demonstrate that our approach achieves greater accuracy than state-of-the-art MIL methods when positive bags are sparse, and performs competitively when they are not. In particular, our approach is the best performing method for image region classification.

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