Challenge: Current models for cross-document co-reference resolution assume that all documents are of the same type or fall under the same theme.
Approach: They propose a task for cross-document cross-domain co-reference resolution (CD2CR) task aims to identify links between entities across heterogeneous document types.
Outcome: The proposed task outperforms current state-of-the-art models on CD2CR in cross-domain, cross-document setting.

Similar Papers

Employing Discourse Coherence Enhancement to Improve Cross-Document Event and Entity Coreference Resolution (2025.acl-long)

Copied to clipboard

Challenge: Existing work on cross-document coreference resolution focuses on within-document events and entities, but cross-doc mentions lack such critical contexts.
Approach: They propose a task to enhance the discourse coherence between two cross-document mentions by adding coherent texts to a document to form a new coherent document.
Outcome: The proposed method outperforms state-of-the-art baselines on three popular datasets.
Event Coreference Data (Almost) for Free: Mining Hyperlinks from Online News (2021.emnlp-main)

Copied to clipboard

Challenge: Annotating CDCR data is laborious and expensive, explaining why existing corpora are small and lack domain coverage.
Approach: They use hyperlinks to extract event coreference data from online news articles . they find that models trained on small subsets of HyperCoref are highly competitive .
Outcome: The proposed system frees up CDCR research from costly human-annotated training data and opens up possibilities beyond English.
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution (2022.acl-short)

Copied to clipboard

Challenge: Existing approaches to solve entity linking (EL) jointly with coreference resolution (coref) a coreferenced cluster can only be linked to a single entity or NIL (i.e., a nonlinkable entity)
Approach: They propose to join entity linking and coreference resolution in a single structured prediction task over directed trees and use a globally normalized model to solve it.
Outcome: The proposed model improves on two datasets with a +5% boost in accuracy compared to standalone models . the proposed model is based on current models that predict a single antecedent for each span to resolve .
A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports (2022.lrec-1)

Copied to clipboard

Challenge: Oftentimes, these findings and devices are referred to multiple times in a single report and are also referred across different reports of a patient.
Approach: They propose a new cross-document coreference resolution (CDCR) dataset for identifying co-referring radiological findings and medical devices across a patient's radiology reports.
Outcome: The proposed dataset contains 5872 mentions (findings and devices) spanning 638 MIMIC-III radiology reports across 60 patients, covering multiple imaging modalities and anatomies.
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution (P19-1)

Copied to clipboard

Challenge: Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task.
Approach: They propose a neural architecture for cross-document coreference resolution by representing an event mention using its lexical span, surrounding context, and relation to other mentions via predicate-arguments structures.
Outcome: The proposed model outperforms the state-of-the-art event coreference model on ECB+ while providing the first entity coreference results on this corpus.
Cross-Document Event Coreference Resolution on Discourse Structure (2023.emnlp-main)

Copied to clipboard

Challenge: Experimental results show that our proposed model outperforms several baselines and achieves the competitive performance with the start-of-the-art baselines.
Approach: They propose to use discourse rhetorical structure constructor to construct tree structures to represent documents and a multi-layer perceptron to capture similarities of event mention pairs.
Outcome: The proposed model outperforms baselines and achieves competitive performance with the start-of-the-art baselines.
Bridging Resolution: A Survey of the State of the Art (2020.coling-main)

Copied to clipboard

Challenge: bridging resolution is an anaphora resolution task that is less studied than entity coreference resolution.
Approach: This paper presents a survey of the current state of research on bridging resolution . it identifies and resolves bridling/associative anaphors, which are anamorphic references to non-identical associated antecedents.
Outcome: The proposed task is more difficult than entity coreference resolution because of the lack of annotated corpora and lack of standardized evaluation protocols.
Learning Word Representations with Cross-Sentence Dependency for End-to-End Co-reference Resolution (D18-1)

Copied to clipboard

Challenge: Existing word embedding models generate word representations by running long short-term memory recurrent neural networks on each sentence of an input article or conversation separately.
Approach: They propose a word embedding model that learns cross-sentence dependency . they use linear sentence linking and attentional sentence linking to learn cross-entry dependency based on context sentences .
Outcome: The proposed model improves end-to-end co-reference resolution by taking knowledge from context sentences and the entire document.
Towards Evaluation of Cross-document Coreference Resolution Models Using Datasets with Diverse Annotation Schemes (2022.lrec-1)

Copied to clipboard

Challenge: Existing cross-document coreference resolution (CDCR) datasets contain event-centric coreference chains of events and entities with identity relations.
Approach: They propose to use a phrasing diversity metric to evaluate lexical diversity of CDCR datasets . they propose to combine CDCR annotation schemes with multiple properties of the coreference chains .
Outcome: The proposed phrasing diversity metric evaluates the CDCR datasets with higher precision.
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities (2022.naacl-main)

Copied to clipboard

Challenge: Identifying related entities and events within and across documents is fundamental to natural language understanding.
Approach: They propose an approach to entity and event coreference resolution using contrastive representation learning.
Outcome: The proposed method achieves state-of-the-art results on key metrics on the ECB+ corpus and is competitive on others.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations