A Study on Improving End-to-End Neural Coreference Resolution
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Many natural language processing applications can benefit from the identification of coreference relations. For example, in information extraction and question answering, recall should in principle increase when the available information is expanded by linking expressions in a text that refer to the same discourse entity. Most current state-of-the-art systems for coreference resolution are based on supervised machine learning, and require (large) amounts of annotated data for training and testing. As these data were lacking for Dutch, the corea project had as its goal the construction of a coreference corpus for Dutch, and the development of an automatic coreference resolution system that used the corpus as training material. In this paper, we present the results of our annotation efforts, and the design of the automatic resolution system. Furthermore, we discuss various experiments that were carried out to determine the accuracy of the resolution system and the potential benefit of incorporating coreference resolution in nlp tasks
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We describe the systems that we developed for the three tracks of the CODI-CRAC 2021 shared task, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. Our team ranked second for entity coreference resolution, first for bridging resolution, and first for discourse deixis resolution.
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We describe the systems that we developed for the three tracks of the CODI-CRAC 2021 shared task, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. Our team ranked second for entity coreference resolution, first for bridging resolution, and first for discourse deixis resolution.
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In the field of information security, a gap exists in the study of coreference resolution of entities. A hybrid method is proposed to solve the problem of coreference resolution in information security. The work consists of... | Find, read and cite all the research you need on Tech Science Press
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Coreference resolution has been mostly investigated within a single document scope, showing impressive progress in recent years based on end-to-end models. However, the more challenging task of cross-document (CD) coreference resolution remained relatively under-explored, with the few recent models applied only to gold mentions. Here, we introduce the first end-to-end model for CD coreference resolution from raw text, which extends the prominent model for within-document coreference to the CD setting. Our model achieves competitive results for event and entity coreference resolution on gold mentions. More importantly, we set first baseline results, on the standard ECB+ dataset, for CD coreference resolution over predicted mentions. Further, our model is simpler and more efficient than recent CD coreference resolution systems, while not using any external resources.
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Coreference resolution has been mostly investigated within a single document scope, showing impressive progress in recent years based on end-to-end models.However, the more challenging task of cross-document (CD) coreference resolution remained relatively under-explored, with the few recent models applied only to gold mentions.Here, we introduce the first end-to-end model for CD coreference resolution from raw text, which extends the prominent model for withindocument coreference to the CD setting.Our model achieves competitive results for event and entity coreference resolution on gold mentions.More importantly, we set first baseline results, on the standard ECB+ dataset, for CD coreference resolution over predicted mentions.Further, our model is simpler and more efficient than recent CD coreference resolution systems, while not using any external resources. 1
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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.
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This paper interprets the concepts of coreference resolution,and introduces the state-of-the-arts in coreference resolution from three aspects:corpus resources,evaluation measures and resolution algorithms.It analyzes the critical problems of coreference resolution:automatic capture and presentation of structured syntactic knowledge,automatic capture and usage of underlying semantic,cross-document coreference resolution.Based on the analysis,it explores its international research trend.
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