Knowledge Tree
coreference resolution
Also known as: co-reference resolution
Facts (13)
Sources
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Nov 4, 2024 3 facts
procedureThe LLM-augmented KG process is structured into two principal stages: (1) synthesizing KGs by applying LLMs to perform coreference resolution, named entity recognition, and relationship extraction to relate entities from input documents; (2) performing tasks on the constructed KG using LLMs, including KG completion to fill gaps, KG question answering to query responses, and KG text generation to develop descriptions of nodes.
claimNamed entity recognition, coreference resolution, and relation extraction are techniques commonly applied to create detailed and accurate knowledge graphs.
referenceSuperGLUE is an extension of the GLUE benchmark designed to evaluate natural language understanding through more challenging tasks such as causal reasoning and coreference resolution.
Patterns in the Transition From Founder-Leadership to Community ... arxiv.org Feb 5, 2026 2 facts
procedureThe pipeline for transforming raw governance documents into structured institutional data consists of: (1) data normalization and pairing rules to align governance snapshots across versions, (2) coreference resolution to reduce pronoun ambiguity, (3) Semantic Role Labeling (SRL) to map sentences to predicate-argument structures, (4) embedding and clustering using BERTopic to capture governance topologies, and (5) evaluation using metrics such as entropy and per-project cluster counts.
procedureTo reduce pronoun ambiguity in governance documents, researchers applied coreference resolution while maintaining a reversible mapping to original offsets, citing Lee et al. (2018) and Jurafsky and Martin.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 2 facts
claimCoreference resolution is a natural language processing task that aims to identify and link different expressions in a text that refer to the same entity.
claimLarge Language Models (LLMs) assist in Knowledge Graph construction by acting as prompts and generators for entity, relation, and event extraction, as well as performing entity linking and coreference resolution.
Unknown source 1 fact
claimThe authors of the paper 'Automated Knowledge Graph Construction using Large Language Models' introduced CoDe-KG, an open-source, end-to-end pipeline designed for extracting sentence-level knowledge graphs by combining robust coreference resolution.
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 1 fact
claimEntity Linking systems face specific challenges including coreference resolution, where entities are referred to indirectly (e.g., via pronouns), and the handling of emerging entities that are recognized but not yet present in the target Knowledge Graph.
How to Improve Multi-Hop Reasoning With Knowledge Graphs and ... neo4j.com Jun 18, 2025 1 fact
procedureDevelopers can address references that point to other documents in RAG systems by using co-reference resolution or pre-processing techniques.
Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org Jul 9, 2024 1 fact
claimThe construction of knowledge graphs is difficult, costly, and time-consuming, requiring steps such as entity extraction, knowledge fusion, and coreference resolution.
A Knowledge-Graph Based LLM Hallucination Evaluation Framework themoonlight.io 1 fact
procedureThe GraphEval framework constructs a Knowledge Graph from LLM output through a four-step pipeline: (1) processing input text, (2) detecting unique entities, (3) performing coreference resolution to retain only specific references, and (4) extracting relations to form triples of (entity1, relation, entity2).
(PDF) Automated Knowledge Graph Construction using Large ... researchgate.net Sep 22, 2025 1 fact
claimCoDe-KG is an open-source, end-to-end pipeline designed for extracting sentence-level knowledge graphs by combining robust coreference resolution with large language models.