extraction of named entity mentions in unstructured text into pre-defined categories
Wikipedia
en.wikipedia.org › wiki › Named-entity_recognition
Named-entity recognition - Wikipedia
September 22, 2025 - Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names (PER), ...
GeeksforGeeks
geeksforgeeks.org › nlp › named-entity-recognition
Named Entity Recognition - GeeksforGeeks
October 4, 2025 - Named Entity Recognition (NER) in NLP focuses on identifying and categorizing important information known as entities in text. These entities can be names of people, places, organizations, dates, etc.
What is your practical NER (Named Entity Recognition) approach? [P]
Check this out: https://hitz-zentroa.github.io/GoLLIE/ ICLR 2024 paper, current SOTA on IE including NER, you write your expected classes and describe them as python dataclasses specified by guidelines and get all the entities, sub-attributes included. Works amazingly! More on reddit.com
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April 4, 2025How to build a NER?
Hi, NER is basically a token level text classification problem, which can be considered to be similar to semantic segmentation in vision tasks, which is pixel level classification. To prepare the dataset, first you need to have a fixed number of labels, like any other classification problem, and each word should be labelled (an label for all words doesn't have an entity). Please ensure no words are left unlabelled. Once you have this dataset, you can try these, based on your dataset aswell: as mentioned in other comments, few-shot learning with LLMs using spacy custom NER model ( Ref: https://medium.com/@mjghadge9007/building-your-own-custom-named-entity-recognition-ner-model-with-spacy-v3-a-step-by-step-guide-15c7dcb1c416 ) BERT token level classifier (Ref: https://huggingface.co/docs/transformers/en/tasks/token_classification ) An RNN or LSTM classifier with some dense embedded features (glove, word2vec etc), and a prediction layer at each time step after the stack of (if multi-layer) RNNs I would suggest you try the 4th one only if you have enough time, otherwise invest more on preparing a good enough custom dataset and work on any of the first 3. More on reddit.com
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April 9, 2024[D] Named Entity Recognition (NER) Libraries
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. More on reddit.com
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January 7, 2023Named Entity Recognition: is there a good guide/tutorial for evaluation/benchmarking?
I don't know about tutorials, but you should check the seqeval library. I also recommend Lignos and Kamyab (2020) about results reproductibility in NER More on reddit.com
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June 26, 2023Videos
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Named Entity Recognition (NER): NLP Tutorial For Beginners - S1 ...
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Best way to do Named Entity Recognition in 2024 with GliNER and ...
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Named Entity Recognition (NER) in Python: Pre-Trained & Custom ...
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Named-Entity Recognition for the Domain of EU Legislation – Dominik ...
Oracle
docs.oracle.com › en-us › iaas › language › using › ner.htm
Named Entity Recognition
October 7, 2025 - Named Entity Recognition (NER) detects named entities in text.
IBM
ibm.com › think › topics › named-entity-recognition
What Is Named Entity Recognition? | IBM
3 weeks ago - Named entity recognition (NER)—also called entity chunking or entity extraction—is a component of natural language processing (NLP) that identifies predefined categories of objects in a body of text.
Kaggle
kaggle.com › code › eneszvo › ner-named-entity-recognition-tutorial
NER - Named Entity Recognition Tutorial
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Coursera
coursera.org › coursera articles › data › ai and machine learning › what is named entity recognition (ner) and how does it work?
What Is Named Entity Recognition (NER) and How Does It Work? | Coursera
March 26, 2025 - Named entity recognition, or NER, is a process that extracts information from text. It’s also referred to as entity chunking, entity extraction, or entity identification. The goal is to identify, sort, and rank pieces of information by importance.
UC Berkeley School of Information
ischool.berkeley.edu › projects › 2025 › context-enriched-named-entity-recognition-ner-identifying-emerging-trends-video
Context-Enriched Named Entity Recognition (NER) for Identifying Emerging Trends in Video Comments | UC Berkeley School of Information
April 18, 2025 - Unlike conventional NER, our approach will incorporate comment-reply hierarchies and semantic embeddings to refine entity recognition and identify emerging trends. Traditional NER struggles with informal language and misspellings in user-generated comments. Named entities in video discussions often appear across multiple comments and replies, requiring a context-aware approach.
SkillWisor
skillwisor.com › 2025 › 06 › 02 › named-entity-recognition-ner-what-why-and-how
Named Entity Recognition (NER): What, Why, and How – SkillWisor
June 2, 2025 - Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP) that involves identifying and classifying named entities within text. These entities represent real-world objects such as people, organizations, locations, ...
GeeksforGeeks
geeksforgeeks.org › nlp › named-entity-recognition-in-nlp
Named Entity Recognition in NLP - GeeksforGeeks
April 28, 2025 - Named Entity Recognition (NER) ... (NLP) that involves the identification and classification of named entities in unstructured text, such as people, organizations, locations, dates, and other relevant information....
arXiv
arxiv.org › html › 2411.05057v1
A Brief History of Named Entity Recognition
November 7, 2024 - A large amount of information in today’s world is now stored in knowledge bases. Named Entity Recognition (NER) is a process of extracting, disambiguation, and linking an entity from raw text to insightful and structured knowledge bases. More concretely, it is identifying and classifying ...
GigaSpaces
gigaspaces.com › data-terms › named-entity-recognition
What is Named Entity Recognition | Gigaspaces AI
September 18, 2025 - Named Entity Recognition (NER) is a task in natural language processing (NLP) whose primary goal is to scan unstructured text, identify specific pieces of information as named entities, and classify them into predefined categories. These include names of people, companies, locations, dates, ...
Nanonets
nanonets.com › blog › named-entity-recognition-with-nltk-and-spacy
A complete guide to Named Entity Recognition (NER) in 2025
January 20, 2025 - However, building these algorithms or computer programs from scratch requires extensive expertise and experimenting, especially for information extraction from text-based data. Hence we rely on NLP (Natural Language Processing) techniques like Named Entity Recognition (NER) to identify and extract the essential entities from any text-based documents.
Saturn Cloud
saturncloud.io › glossary › named-entity-recognition
Named Entity Recognition (NER) | Saturn Cloud
April 14, 2023 - Named Entity Recognition (NER) is a subtask of information extraction in natural language processing that aims to identify and classify named entities within a given text, such as people, organizations, locations, dates, and other entity types.
Dataknowsall
dataknowsall.com › blog › ner.html
An Accessible Guide to Named Entity Recognition
March 5, 2024 - Named Entity Recognition or NER is a technique for identifying and classifying named entities in text. These entities are a level above Part of Speech Tagging and Noun Phrase Chunking where instead of identifying grammatical parts; it's identifying ...
Elastic
elastic.co › elastic docs › explore and analyze › machine learning › nlp › examples › named entity recognition
Named entity recognition | Elastic Docs
Using the example text "Elastic is headquartered in Mountain View, California.", the model finds three entities: an organization "Elastic", and two locations "Mountain View" and "California". You can perform bulk inference on documents as they are ingested by using an inference processor in your ingest pipeline. The novel Les Misérables by Victor Hugo is used as an example for inference in the following example. Download the novel text split by paragraph as a JSON file, then upload it by using the Data Visualizer. Give the new index the name les-miserables when uploading the file.
Reddit
reddit.com › r/machinelearning › what is your practical ner (named entity recognition) approach? [p]
r/MachineLearning on Reddit: What is your practical NER (Named Entity Recognition) approach? [P]
April 4, 2025 -
Hi all,
I'm working on a Flutter app that scans food products using OCR (Google ML Kit) to extract text from an image, recognizes the language and translate it to English. This works. The next challenge is however structuring the extracted text into meaningful parts, so for example:
Title
Nutrition Facts
Brand
etc.
The goal would be to extract those and automatically fill the form for a user.
Right now, I use rule-based parsing (regex + keywords like "Calories"), but it's unreliable for unstructured text and gives messy results. I really like the Google ML kit that is offline, so no internet and no subscriptions or calls to an external company. I thought of a few potential approaches for extracting this structured text:
Pure regex/rule-based parsing → Simple but fails with unstructured text. (so maybe not the best solution)
Make my own model and train it to perform NER (Named Entity Recognition) → One thing, I have never trained any model and am a noob in this AI / ML thing.
External APIs → Google Cloud NLP, Wit.ai, etc. (but this I really would prefer to avoid to save costs)
Which method would you recommend? I am sure I maybe miss some approach and would love to hear how you all tackle similar problems! I am willing to spend time btw into AI/ML but of course I'm looking to spend my time efficient.
Any reference or info is highly appreciated!
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Check this out: https://hitz-zentroa.github.io/GoLLIE/ ICLR 2024 paper, current SOTA on IE including NER, you write your expected classes and describe them as python dataclasses specified by guidelines and get all the entities, sub-attributes included. Works amazingly!
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Cool project! For local/offline NER, you might try fine-tuning a small model like DistilBERT using something like ONNX or TensorFlow Lite for deployment. Start by labeling ~500–1000 examples and training with spaCy—it’s pretty beginner-friendly and gives solid results for this kind of semi-structured data.
Medium
medium.com › mysuperai › what-is-named-entity-recognition-ner-and-how-can-i-use-it-2b68cf6f545d
What is named entity recognition (NER) and how can I use it? | by Christopher Marshall | super.AI | Medium
June 2, 2020 - Named entity recognition (NER) — sometimes referred to as entity chunking, extraction, or identification — is the task of identifying and categorizing key information (entities) in text.
Flexibench
flexibench.io › blog › named-entity-recognition-ner-identifying-entities-in-text
Named Entity Recognition (NER): Identifying Entities in Text
Named Entity Recognition (NER) is the task of identifying and classifying real-world objects mentioned in text into predefined categories.
Turing
turing.com › kb › a-comprehensive-guide-to-named-entity-recognition
A Comprehensive Guide to Named Entity Recognition (NER)
Named entity recognition (NER) is a form of natural language processing (NLP) that involves extracting and identifying essential information from text. The information that is extracted and categorized is called entity.
Label Rer
labellerr.com › blog › beginners-guide-to-named-entity-recognition-ner-with-machine-learning
Named Entity Recognition: Guide to NER Techniques
July 16, 2025 - One technique for extracting information from text in natural language processing (NLP) is named entity recognition (NER).