Antecedent selection techniques for high-recall roreference resolution

We investigate methods to improve the recall in coreference resolution by also trying to resolve those definite descriptions where no earlier mention of the referent shares the same lexical head (coreferent bridging). The problem, which is notably harder than identifying coreference relations among mentions which have the same lexical head, has been tackled with several rather different approaches, and we attempt to provide a meaningful classification along with a quantitative comparison. Based on the different merits of the methods, we discuss possibilities to improve them and show how they can be effectively combined

Identity and Granularity of Events in Text

In this paper we describe a method to detect event descrip- tions in different news articles and to model the semantics of events and their components using RDF representations. We compare these descriptions to solve a cross-document event coreference task. Our com- ponent approach to event semantics defines identity and granularity of events at different levels. It performs close to state-of-the-art approaches on the cross-document event coreference task, while outperforming other works when assuming similar quality of event detection. We demonstrate how granularity and identity are interconnected and we discuss how se- mantic anomaly could be used to define differences between coreference, subevent and topical relations.Comment: Invited keynote speech by Piek Vossen at Cicling 201

Robustness in Coreference Resolution

Coreference resolution is the task of determining different expressions of a text that refer to the same entity. The resolution of coreferring expressions is an essential step for automatic interpretation of the text. While coreference information is beneficial for various NLP tasks like summarization, question answering, and information extraction, state-of-the-art coreference resolvers are barely used in any of these tasks. The problem is the lack of robustness in coreference resolution systems. A coreference resolver that gets higher scores on the standard evaluation set does not necessarily perform better than the others on a new test set. In this thesis, we introduce robustness in coreference resolution by (1) introducing a reliable evaluation framework for recognizing robust improvements, and (2) proposing a solution that results in robust coreference resolvers. As the first step of setting up the evaluation framework, we introduce a reliable evaluation metric, called LEA, that overcomes the drawbacks of the existing metrics. We analyze LEA based on various types of errors in coreference outputs and show that it results in reliable scores. In addition to an evaluation metric, we also introduce an evaluation setting in which we disentangle coreference evaluations from parsing complexities. Coreference resolution is affected by parsing complexities for detecting the boundaries of expressions that have complex syntactic structures. We reduce the effect of parsing errors in coreference evaluation by automatically extracting a minimum span for each expression. We then emphasize the importance of out-of-domain evaluations and generalization in coreference resolution and discuss the reasons behind the poor generalization of state-of-the-art coreference resolvers. Finally, we show that enhancing state-of-the-art coreference resolvers with linguistic features is a promising approach for making coreference resolvers robust across domains. The incorporation of linguistic features with all their values does not improve the performance. However, we introduce an efficient pattern mining approach, called EPM, that mines all feature-value combinations that are discriminative for coreference relations. We then only incorporate feature-values that are discriminative for coreference relations. By employing EPM feature-values, performance improves significantly across various domains