In this paper, we define the problem of coreference resolution in text as one of clustering with pairwise constraints where human experts are asked to provide pairwise constraints (pairwise judgments of coreferentiality) to guide the clustering process. Positing that these pairwise judgments are easy to obtain from humans given the right context, we show that with significantly lower number of pairwise judgments and feature-engineering effort, we can achieve competitive coreference performance. Further, we describe an active learning strategy that minimizes the overall number of such pairwise judgments needed by asking the most informative questions to human experts at each step of coreference resolution. We evaluate this hypothesis and our algorithms on both entity and event coreference tasks and on two languages.
We investigate new methods for creating and applying ensembles for coreference resolution. While existing ensembles for coreference resolution are typically created using different learning algorithms, clustering algorithms or training sets, we harness recent advances in coreference modeling and propose to create our ensemble from a variety of supervised coreference models. However, the presence of pairwise and non-pairwise coreference models in our ensemble presents a challenge to its application: it is not immediately clear how to combine the coreference decisions made by these models. We investigate different methods for applying a model-heterogeneous ensemble for coreference resolution. Empirical results on the ACE data sets demonstrate the promise of ensemble approaches: all ensemble-based systems significantly outperform the best member of the ensemble.
This paper introduces a new structured model for learning anaphoricity detection and coreference resolution in a joint fashion. Specifically,we use a latent tree to represent the full coreference and anaphoric structure of a document at a global level, and we jointly learn the parameters of the two models using a version of the structured perceptron algorithm. Our joint structured model is further refined by the use of pairwise constraints which help the model to capture accurately certain patterns of coreference. Our experiments on the CoNLL-2012 English datasets show large improvements in both coreference resolution and anaphoricity detection, compared to various competing architectures. Our best coreference system obtains a CoNLL score of 81.97 on gold mentions, which is to date the best score reported on this setting.
Coreference resolution is the problem of clustering mentions into entities and is very critical for natural language understanding. This paper studies the problem of coreference resolution in the context of the important domain of clinical text. Clinical text is unique because it requires significant use of domain knowledge to support coreference resolution. It also has specific discourse characteristics which impose several constraints on coreference decisions. We present a principled framework to incorporate knowledge-based constraints in the coreference model. We also show that different pronouns behave quite differently, necessitating the development of distinct ways for resolving different pronouns. Our methods result in significant performance improvements and we report the best results on a clinical corpora that has been used in coreference shared tasks. Moreover, for the first time, we report the results for end-to-end coreference resolution on this corpora.
Although information extraction and coreference resolution appear together in many applications, most current systems perform them as ndependent steps. This paper describes an approach to integrated inference for extraction and coreference based on conditionally-trained undirected graphical models. We discuss the advantages of conditional probability training, and of a coreference model structure based on graph partitioning. On a data set of research paper citations, we show significant reduction in error by using extraction uncertainty to improve coreference citation matching accuracy, and using coreference to improve the accuracy of the extracted fields.