A brief survey on recent advances in coreference resolution

Liu, Ruicheng; Mao, Rui; Luu, Anh Tuan; Wang, Zhaoxia

doi:10.1007/s10462-023-10506-3

Cited by 7 publications

(5 citation statements)

References 96 publications

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“…As already indicated above, automatic coreference resolution does show good results in extracting identity clusters from a document [5]. However, we have seen that there exist near identity relations between expressions, potentially even across documents, that would be mostly overseen by standard coreference resolution approaches [6].…”

Section: Diverse Cross-document Coreference and Media Bias Analysissupporting

confidence: 54%

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Code Book for the Annotation of Diverse Cross-Document Coreference of Entities in News Articles

2023

Int J Media Net

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This paper presents a scheme for annotating coreference across news articles, extending beyond traditional identity relations by also considering near-identity and bridging relations. It includes a precise description of how to set up Inception, a respective annotation tool, how to annotate entities in news articles, connect them with diverse coreferential relations, and link them across documents to Wiki data’s global knowledge graph. This multi-layered annotation approach is discussed in the context of the problem of media bias. Our main contribution lies in providing a methodology for creating a diverse cross-document coreference corpus which can be applied to the analysis of media bias by word-choice and labelling

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Section: Diverse Cross-document Coreference and Media Bias Analysissupporting

confidence: 54%

“…This includes facets like: * a certain role or function performed by an entity. Consider example (5).…”

Section: A Metonymy (Met)mentioning

confidence: 99%

Code Book for the Annotation of Diverse Cross-Document Coreference of Entities in News Articles

2023

Int J Media Net

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“…It is a crucial task related to many NLP applications, including sentiment analysis (characterizing the sentiment of a text), summarization, translation, question answering, and Named Entity Recognition. Unfortunately, despite its importance, the progress in Coreference Resolution has been the slowest compared to those other fields [43].…”

Section: Coreference Resolutionmentioning

confidence: 99%

A Survey on Challenges and Advances in Natural Language Processing with a Focus on Legal Informatics and Low-Resource Languages

Krasadakis,

Sakkopoulos,

Verykios

2024

Electronics

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The field of Natural Language Processing (NLP) has experienced significant growth in recent years, largely due to advancements in Deep Learning technology and especially Large Language Models. These improvements have allowed for the development of new models and architectures that have been successfully applied in various real-world applications. Despite this progress, the field of Legal Informatics has been slow to adopt these techniques. In this study, we conducted an extensive literature review of NLP research focused on legislative documents. We present the current state-of-the-art NLP tasks related to Law Consolidation, highlighting the challenges that arise in low-resource languages. Our goal is to outline the difficulties faced by this field and the methods that have been developed to overcome them. Finally, we provide examples of NLP implementations in the legal domain and discuss potential future directions.

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“…Early research on ECR was mainly based on rule-based methods [14][15][16][17][18], but with the widespread application of machine learning methods. Recent research has mainly used traditional machine learning and neural network methods to complete ECR tasks [1]. A binary classifier is trained to determine whether two pieces of text refer to the same realworld event, and then coreferential events are optimized by clustering [19].…”

Section: Related Workmentioning

confidence: 99%

“…The task holds significant importance in enabling a variety of downstream applications, including information extraction, question answering, text summarization, etc. [1,2]. For example, in the application of information extraction, ECR can help to build more coherent and complete knowledge graphs or databases by linking different mentions of the same event and correctly identifying and linking relevant information to answer questions accurately [3].…”

Section: Introductionmentioning

confidence: 99%

Short Text Event Coreference Resolution Based on Context Prediction

Yong,

Zeng,

Dai

et al. 2024

Applied Sciences

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Event coreference resolution is the task of clustering event mentions that refer to the same entity or situation in text and performing operations like linking, information completion, and validation. Existing methods model this task as a text similarity problem, focusing solely on semantic information, neglecting key features like event trigger words and subject. In this paper, we introduce the event coreference resolution based on context prediction (ECR-CP) as an alternative to traditional methods. ECR-CP treats the task as sentence-level relationship prediction, examining if two event descriptions can create a continuous sentence-level connection to identify coreference. We enhance ECR-CP with a fusion coding model (ECR-CP+) to incorporate event-specific structure and semantics. The model identifies key text information such as trigger words, argument roles, event types, and tenses via an event extraction module, integrating them into the encoding process as auxiliary features. Extensive experiments on the benchmark CCKS 2021 dataset demonstrate that ECR-CP and ECR-CP+ outperform existing methods in terms of precision, recall, and F1 Score, indicating their superior performance.

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A brief survey on recent advances in coreference resolution

Cited by 7 publications

References 96 publications

Code Book for the Annotation of Diverse Cross-Document Coreference of Entities in News Articles

Code Book for the Annotation of Diverse Cross-Document Coreference of Entities in News Articles

A Survey on Challenges and Advances in Natural Language Processing with a Focus on Legal Informatics and Low-Resource Languages

Short Text Event Coreference Resolution Based on Context Prediction

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