arXiv
arxiv.org › html › 2411.05057v1
A Brief History of Named Entity Recognition
November 7, 2024 - AllenNLP Gardner et al. (2017) : The named entity recognition model identifies named entities such as people, locations, organizations, etc. in the input text. The baseline in this model is based on ELMO Peters et al.
Tonic.ai
tonic.ai › guides › named-entity-recognition-models
What Is Named Entity Recognition (NER): How It Works & More | Tonic.ai
Leverage proprietary NER models ... AI innovation. ... Named Entity Recognition is an NLP task that identifies and classifies words or phrases in text into predefined categories (e.g....
Published March 11, 2025
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What is Named Entity Recognition (NER)?
Named entity recognition (NER) is a subfield within natural language processing (NLP) that focuses on identifying and classifying specific data points from textual content. NER works with salient details of the text, known as named entities — single words, phrases, or sequences of words — by identifying and categorizing them into predefined groups.
altexsoft.com
altexsoft.com › blog › named-entity-recognition
Named Entity Recognition: The Mechanism, Methods, Use Cases,
What are the approaches to NER?
The main ones are rule-based, machine learning-based, and deep learning-based approaches to perform named entity recognition.
altexsoft.com
altexsoft.com › blog › named-entity-recognition
Named Entity Recognition: The Mechanism, Methods, Use Cases,
IBM
ibm.com › think › topics › named-entity-recognition
What Is Named Entity Recognition? | IBM
3 weeks ago - Transformer networks, particularly the BERT (Bidirectional Encoder Representations from Transformers) model, have had a significant impact on NER. Using a self-attention mechanism that weighs the importance of different words, BERT accounts for the full context of a word by looking at the words that come before and after it. The first step of NER is to aggregate a dataset of annotated text. The dataset should contain examples of text where named entities are labeled or marked, indicating their types.
Hugging Face
huggingface.co › dslim › bert-base-NER
dslim/bert-base-NER · Hugging Face
bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task.
AssemblyAI
assemblyai.com › blog › 6-best-named-entity-recognition-apis-entity-detection
6 best named entity recognition APIs for entity detection
AI model-based Named Entity Recognition uses trained neural networks to understand the semantic and syntactic relationships between words and phrases.
Turing
turing.com › kb › a-comprehensive-guide-to-named-entity-recognition
A Comprehensive Guide to Named Entity Recognition (NER)
Any NER model has a two-step process: i) detect a named entity and ii) categorize the entity. The first step for named entity recognition is detecting an entity or keyword from the given input text.
GeeksforGeeks
geeksforgeeks.org › nlp › named-entity-recognition
Named Entity Recognition - GeeksforGeeks
October 4, 2025 - NER identifies named entities like people, organizations, places, dates, and quantities—not parts of speech like adjectives. In the sentence “Amazon is expanding rapidly”, what does NER classify "Amazon" as? ... Here "Amazon" refers to the company, so NER classifies it as an Organization. Which method of NER uses probabilistic models like Conditional Random Fields (CRF)?
Wikipedia
en.wikipedia.org › wiki › Named-entity_recognition
Named-entity recognition - Wikipedia
September 22, 2025 - NER systems have been created that use linguistic grammar-based techniques as well as statistical models such as machine learning. State of the art systems may incorporate multiple approaches. GATE supports NER across many languages and domains out of the box, usable via a graphical interface and a Java API. OpenNLP includes rule-based and statistical named-entity recognition.
AltexSoft
altexsoft.com › blog › named-entity-recognition
Named Entity Recognition: The Mechanism, Methods, Use Cases,
November 1, 2023 - NeuroNER is a program designed specifically for neural network-based named entity recognition. How NeuroNER works. Source: NeuroNER.com · NeuroNER enables users to create or modify annotations for a new or existing corpus, ensuring tailored and precise entity recognition outcomes. DeepPavlov is an open-source library for conversational AI based on ML libraries like TensorFlow and Keras, offering a collection of pre-trained NER models suitable for deep learning enthusiasts.
DataCamp
datacamp.com › blog › what-is-named-entity-recognition-ner
What is Named Entity Recognition (NER)? Methods, Use Cases, and Challenges | DataCamp
September 13, 2023 - They might struggle with large or diverse datasets due to the rigidity of predefined rules. Transitioning from manual rules, statistical methods employ models like Hidden Markov Models (HMM) or Conditional Random Fields (CRF).
Analytics Vidhya
analyticsvidhya.com › home › a beginner’s introduction to ner (named entity recognition)
A Beginner's Introduction to NER (Named Entity Recognition)
March 20, 2024 - This article will give you a brief idea about Named Entity recognition , a popular method that is used for recognizing entities that are present in a text document. This article is targeted at beginners in the field of NLP. Towards the end of the article, you will see how pre-trained NER models are implemented in practical use cases.
Encord
encord.com › blog › named-entity-recognition
What Is Named Entity Recognition? Selecting the Best Tool to Transform Your Model Training Data
January 22, 2025 - ... Named Entity Recognition (NER) is a Natural Language Processing (NLP) technique used to identify and classify named entities in unstructured text into predefined categories such as Person, Organization, Location, Date, and more.
Stanford NLP Group
nlp.stanford.edu › software › CRF-NER.html
Software > Stanford Named Entity Recognizer (NER)
It comes with well-engineered feature extractors for Named Entity Recognition, and many options for defining feature extractors. Included with the download are good named entity recognizers for English, particularly for the 3 classes (PERSON, ORGANIZATION, LOCATION), and we also make available on this page various other models for different languages and circumstances, including models trained on just the CoNLL 2003 English training data.
ArcGIS
doc.arcgis.com › en › pretrained-models › latest › text › introduction-to-named-entity-recognition.htm
Introduction to the model—ArcGIS pretrained models | Documentation
An entity may refer to a word or a sequence of words, such as the name of an organization, person, or country, or date, or time, in the text. This pretrained model detects entities from the text and classifies them into the predetermined category. Named entity recognition (NER) can be useful ...
arXiv
arxiv.org › abs › 2402.17447
[2402.17447] Deep Learning Based Named Entity Recognition Models for Recipes
June 6, 2024 - We conclude that few-shot prompting on LLMs has abysmal performance, whereas the fine-tuned spaCy-transformer emerges as the best model with macro-F1 scores of 95.9%, 96.04%, and 95.71% for the manually-annotated, augmented, and machine-annotated datasets, respectively. From: Ganesh Bagler Prof [view email] [v1] Tue, 27 Feb 2024 12:03:56 UTC (938 KB) [v2] Thu, 6 Jun 2024 07:41:21 UTC (938 KB) ... View a PDF of the paper titled Deep Learning Based Named Entity Recognition Models for Recipes, by Mansi Goel and 8 other authors
Reddit
reddit.com › r/machinelearning › [d] named entity recognition (ner) libraries
r/MachineLearning on Reddit: [D] Named Entity Recognition (NER) Libraries
January 7, 2023 -
Hi everyone, I have to cluster a large chunk of textual conversational business data to find relevant topics in it.
Since there is lot of abstract info in every text like phone, url, numbers, email, name, etc., I have done some basic NER using regex and spacy NER to tag such info and make the texts more generic and canonicalized.
But there are some things like product names, raw materials, brand/model, company, etc. which couldn't be tagged. Also, the accuracy of regex and spacy NER isn't high enough.
Can anyone suggest a good python NER library, which is accurate and fast enough, preferably has pre-trained models and can tag diverse fields.
Thanks.
Top answer 1 of 5
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If spaCy’s NER isn’t picking up what you need, you’ll probably need to look into creating your own annotations and fine tuning a model or training a custom model. It isn’t too hard using BIO/BILOU tags. Things like “raw materials” and particularly niche models and brands are unlikely to be picked up by off the shelf solutions.
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In 2024 the best solution in order to perform NER on any sort of tag without data labelling it to use a generative model. For example you could load a relatively small generative model on your machine like Phi 3 with Ollama and come up with the right prompt to extract the right entities. You could also simply plug into an AI API like NLP Cloud's NER API . In general, the bigger the model, the better. But of course you have to carefully watch costs...
Kairntech
kairntech.com › home › blog › the complete guide to named entity recognition (ner): methods, tools, and use cases
The Complete Guide to Named Entity Recognition (NER) - Kairntech
June 3, 2025 - While NLP encompasses all techniques to process and understand language, NER focuses specifically on extracting and classifying named entities from text. ... BERT is a large language model that can be fine-tuned for NER tasks. NER, on the other hand, is a goal — extracting entities — which BERT can help achieve when integrated into a recognition pipeline.
Papers with Code
paperswithcode.com › task › named-entity-recognition-ner
Named Entity Recognition (NER)
PaddleOCR-VL, a vision-language model combining NaViT-style dynamic resolution and ERNIE, achieves state-of-the-art performance in document parsing and element recognition with high efficiency.
arXiv
arxiv.org › abs › 2309.14084
[2309.14084] Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challenges
September 25, 2023 - The narrative accentuates domain-specific NER models, tailored for intricate areas like finance, legal, and healthcare, emphasizing their specialized adaptability. Additionally, the research delves into cutting-edge paradigms including reinforcement learning, innovative constructs like E-NER, and the interplay of Optical Character Recognition (OCR) in augmenting NER capabilities.
ArcGIS
developers.arcgis.com › python › latest › guide › how-named-entity-recognition-works
Named Entity Extraction Workflow with | ArcGIS API for Python | Esri Developer
These are BERT[4], RoBERTa, DistilBERT, ALBERT, FlauBERT, CamemBERT, XLNet, XLM, XLM-RoBERTa, ELECTRA, Longformer and MobileBERT. Some consideration has to be made to pick the right transformer architecture for the problem at hand. Some models like BERT, RoBERTa, XLNET, XLM-RoBERTa are highly ...