Knowledge Tree
Named Entity Recognition (NER)
Also known as: NER, Named Entity Recognition (NER), Named Entity Recognition
Facts (59)
Sources
Combining large language models with enterprise knowledge graphs frontiersin.org Aug 26, 2024 15 facts
claimInaccurate Named Entity Recognition and Relation Extraction prompting results can be corrected through active learning techniques (Wu et al., 2022) or by distilling large Pre-trained Language Models into smaller models for specific tasks (Agrawal et al., 2022).
referenceThe paper 'Named entity recognition as dependency parsing' by Yu et al. (2020) proposes framing the task of named entity recognition as a dependency parsing problem.
claimRelation extraction (RE) identifies and categorizes relationships between entities in unstructured text to expand knowledge graph structures, while named entity recognition (NER) focuses on recognizing, classifying, and linking entities in text to a knowledge base.
claimSupervised methods for named entity recognition and relation extraction typically involve a pretraining stage followed by zero-shot learning or the use of specialized architectures and training setups.
claimModeling Named Entity Recognition (NER) or Relation Extraction (RE) as classification problems forces models to predict a specific entity or relation, which leaves little room for uncertainty.
claimWan et al. (2022) focused on mitigating distant supervision label noise and improving results in Named Entity Recognition.
claimPrompting with large Large Language Models (like GPTs) can underperform in Named Entity Recognition compared to fine-tuned smaller Pre-trained Language Models (like BERT derivations), especially when more training data is available (Gutierrez et al., 2022; Keloth et al., 2024; Pecher et al., 2024; Törnberg, 2024).
referenceThe paper 'Named entity recognition datasets: a classification framework' by Zhang and Xiao (2024) proposes a classification framework for datasets used in named entity recognition.
claimDistant supervision (DS) is an automated data labeling technique that aligns knowledge bases with raw corpora to produce annotated data, used to address the lack of large annotated corpora for relation extraction and named entity recognition.
procedurePrompting for Named Entity Recognition involves using entity definitions, questions, sentences, and output examples to guide Large Language Models in understanding entity types and extracting answers (Ashok and Lipton, 2023; Kholodna et al., 2024).
procedureLiang et al. (2020) proposed a two-stage method for Named Entity Recognition using distant supervision: first, fine-tuning a Large Language Model (LLM) on distant supervision labels, followed by teacher-student system self-training using pseudo soft labels to improve performance.
claimDistant supervision (DS) methods for Named Entity Recognition (NER) involve tagging text corpora using external knowledge sources such as dictionaries, knowledge bases, or knowledge graphs.
measurementPopular Named Entity Recognition (NER) datasets have limited entity classes: the CoNLL 2003 dataset contains four entity types, the ACE 2005 dataset contains seven, and the Ontonotes v5 dataset contains 18 entities, as noted by Zhang and Xiao (2024).
referenceRecent literature identifies two primary approaches to named entity recognition and relation extraction: creating large training sets with hand-curated or extensive automatic annotations to fine-tune large language models, or using precise natural language instructions to replace domain knowledge with prompt engineering.
claimHuman-in-the-loop (HITL) methods effectively handle scarce or sparse data for Named Entity Recognition (NER) (Shen et al., 2017), address mislabeling (Muthuraman et al., 2021), and enhance data processing, model training, and inference stages of the machine learning pipeline (Zhang et al., 2019; Klie et al., 2020; Wu et al., 2022).
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 14 facts
claimThe Named Entity Recognition (NER) approaches AGDISTIS, TagME, and WAT utilize prefix tries or inverted indexing for efficient dictionary lookups.
claimOff-the-shelf named-entity recognition tools do not provide canonicalized identifiers for extracted mentions, necessitating a second step to link mentions to existing entities in a knowledge graph or to assign new identifiers.
claimEntity dictionaries used for Named Entity Recognition (NER) are often incomplete.
referenceFan et al. (2020) utilized deep learning-based named entity recognition for knowledge graph construction specifically applied to geological hazards.
claimDeep Neural Networks are popular for Named Entity Recognition (NER) because they require less human interaction compared to Conditional Random Fields (CRF), which require extensive feature engineering.
claimEntity Linking algorithms utilize various features for disambiguation, including the confidence score of the Named Entity Recognition (NER) tool, the similarity between the mention and the candidate entity, and the overlap across mentions.
claimMachine learning methods are used for Named Entity Recognition (NER) to identify 'emerging entities' that are unknown to a knowledge base.
referenceJ. Li, A. Sun, J. Han, and C. Li authored 'A Survey on Deep Learning for Named Entity Recognition,' which was published in IEEE Transactions on Knowledge and Data Engineering.
claimLong Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN), are used for Named Entity Recognition (NER) tasks.
claimWhile most widely used named-entity recognition scenarios only determine mentions of a handful of types, knowledge graphs typically contain hundreds or thousands of types.
procedureText-based knowledge representation involves three main steps: named-entity recognition, entity linking, and relation extraction.
claimKnowledge Extraction is the process of deriving structured information and knowledge from unstructured or semi-structured data using techniques such as named entity recognition, entity linking, relation extraction, and the canonicalization of entity and relation identifiers.
claimNamed-entity recognition (NER) is the process of demarcating the locations of entity mentions within an input text.
procedureUsing a dictionary (also called a lexicon or gazetteer) is a reliable and simple method to detect entity mentions in text, as it maps labels of desired entities to identifiers in a knowledge graph, effectively performing named-entity recognition and entity linking in a single step.
The construction and refined extraction techniques of knowledge ... nature.com Feb 10, 2026 5 facts
claimPre-trained models like BERT optimize performance in Named Entity Recognition (NER) tasks, particularly in cross-lingual settings, while domain-specific fine-tuning enhances the recognition of specialized terminology.
claimThe BiLSTM-CRF model improves sequence labeling in Named Entity Recognition (NER) tasks through contextual learning.
claimUniversalNER introduces targeted distillation from LLMs to improve open Named Entity Recognition (NER) under limited supervision.
referenceGuillaume Lample et al. published 'Neural Architectures for Named Entity Recognition' in the Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 260–270, in 2016.
claimNeural network-based techniques for knowledge graph construction, such as SpaCy, NLTK, and ltp, utilize a blend of rules and statistical models for Named Entity Recognition (NER) tasks.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 5 facts
referenceMónica Marrero, Julián Urbano, Sonia Sánchez-Cuadrado, Jorge Morato, and Juan Miguel Gómez-Berbís published 'Named entity recognition: fallacies, challenges and opportunities'.
referenceArya Roy published 'Recent trends in named entity recognition (ner)' as an arXiv preprint (arXiv:2101.11420) in 2021.
claimMethods such as Graph Neural Networks (GNNs), Named Entity Recognition (NER), link prediction, and relation extraction fall into the Neuro[Symbolic] category because they leverage symbolic relationships like ontologies or graphs to enhance neural processing.
claimGraph Neural Networks (GNNs) are effective in named entity recognition (NER) by leveraging graph representations to capture contextual dependencies and relationships between entities in text.
referenceNatural language processing (NLP) technologies include retrieval-augmented generation (RAG), sequence-to-sequence models, semantic parsing, named entity recognition (NER), and relation extraction.
A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Nov 4, 2024 5 facts
procedureThe process of integrating KGs with LLMs begins with data preparation, which involves extracting entities and relationships from KGs using techniques like Named Entity Recognition (NER) and relation extraction.
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.
claimBERT utilizes deep contextual understanding for question answering and named entity recognition (NER) task completion.
claimLarge Language Models (LLMs) perform sentiment classification, topic categorization, and named entity recognition (NER) to identify names, dates, and locations.
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org May 20, 2024 4 facts
claimJointly performing Named Entity Recognition and Relationship Extraction reduces error propagation and improves overall performance in Knowledge Graph construction.
claimNamed Entity Recognition and Relationship Extraction are key tasks for constructing Knowledge Graphs from unstructured text.
claimJointly performing Named Entity Recognition and Relationship Extraction reduces error propagation and improves overall performance in Knowledge Graph construction.
claimNamed Entity Recognition and Relationship Extraction are key tasks for constructing Knowledge Graphs from unstructured text.
Integrating Knowledge Graphs into RAG-Based LLMs to Improve ... thesis.unipd.it 3 facts
procedureThe proposed method for integrating knowledge graphs with LLMs utilizes Named Entity Recognition (NER) and Named Entity Linking (NEL) combined with SPARQL queries directed at the DBpedia knowledge graph.
procedureThe proposed method in the thesis integrates knowledge graphs with Large Language Models by combining Named Entity Recognition (NER) and Named Entity Linking (NEL) with SPARQL queries to the DBpedia knowledge graph.
procedureThe proposed fact-checking method utilizes a system that combines Named Entity Recognition (NER) and Named Entity Linking (NEL) with SPARQL queries directed at the DBpedia knowledge graph.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 3 facts
referenceTOPT (Zhang et al., 2024a) is a task-oriented pre-training model that utilizes Large Language Models to generate task-specific knowledge corpora to enhance domain adaptability and Named Entity Recognition sensitivity.
referenceGPT-NER (Wang S. et al., 2023) improves Named Entity Recognition by converting the sequence labeling task into a text generation task using special markers to identify entities.
referenceWang et al. (2023) developed 'GPT-NER', a method for named entity recognition using large language models.
Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org Jul 9, 2024 2 facts
claimKGs constructed by text mining utilize subtasks like named entity recognition and relationship extraction to extract graph data from text, but these KGs are limited by the quality and scope of the input data.
claimThe integration of Large Language Models and Knowledge Graphs improves performance in Natural Language Processing (NLP) tasks, specifically named entity recognition and relation classification.
How NebulaGraph Fusion GraphRAG Bridges the Gap Between ... nebula-graph.io Jan 27, 2026 1 fact
claimBuilding a knowledge graph traditionally requires NLP expertise in named entity recognition, relationship extraction, and entity linking, alongside significant volumes of labeled data and model fine-tuning.
The State of the Art on Knowledge Graph Construction from Text zenodo.org May 5, 2022 1 fact
referenceThe presentation titled 'The State of the Art on Knowledge Graph Construction from Text: Named Entity Recognition and Relation Extraction Perspectives' covers benchmark dataset resources and neural models for knowledge graph construction tasks.
Addressing common challenges with knowledge graphs - SciBite scibite.com 1 fact
claimSciBite's TERMite Named Entity Recognition (NER) engine identifies scientific entities in unstructured text and aligns them to unique identifiers in ontologies to produce structured data.