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Báo cáo khoa học: "Enforcing Transitivity in Coreference Resolution"

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A desirable quality of a coreference resolution system is the ability to handle transitivity constraints, such that even if it places high likelihood on a particular mention being coreferent with each of two other mentions, it will also consider the likelihood of those two mentions being coreferent when making a final assignment. This is exactly the kind of constraint that integer linear programming (ILP) is ideal for, but, surprisingly, previous work applying ILP to coreference resolution has not encoded this type of constraint. We train a coreference classifier over pairs of mentions, and show how to encode this type. | Enforcing Transitivity in Coreference Resolution Jenny Rose Finkel and Christopher D. Manning Department of Computer Science Stanford University Stanford CA 94305 j rfinkel manning @cs.Stanford.edu Abstract A desirable quality of a coreference resolution system is the ability to handle transitivity constraints such that even if it places high likelihood on a particular mention being coreferent with each of two other mentions it will also consider the likelihood of those two mentions being coreferent when making a final assignment. This is exactly the kind of constraint that integer linear programming ILP is ideal for but surprisingly previous work applying ILP to coreference resolution has not encoded this type of constraint. We train a coreference classifier over pairs of mentions and show how to encode this type of constraint on top of the probabilities output from our pairwise classifier to extract the most probable legal entity assignments. We present results on two commonly used datasets which show that enforcement of transitive closure consistently improves performance including improvements of up to 3.6 using the b3 scorer and up to 16.5 using cluster f-measure. 1 Introduction Much recent work on coreference resolution which is the task of deciding which noun phrases or mentions in a document refer to the same real world entity builds on Soon et al. 2001 . They built a decision tree classifier to label pairs of mentions as coreferent or not. Using their classifier they would build up coreference chains where each mention was linked up with the most recent previous mention that the classifier labeled as coreferent if such a mention existed. Transitive closure in this model was done implicitly. If John Smith was labeled coreferent with Smith and Smith with Jane Smith then John Smith and Jane Smith were also coreferent regardless of the classifier s evaluation of that pair. Much work that followed improved upon this strategy by improving the features Ng and .