GeeksforGeeks
geeksforgeeks.org › python › python-named-entity-recognition-ner-using-spacy
Python | Named Entity Recognition (NER) using spaCy - GeeksforGeeks
July 12, 2025 - Efficient pipeline processing: It can efficiently handle text processing tasks, including tokenization, part-of-speech tagging, dependency parsing and named entity recognition. Customizability: We can train custom models or manually defining new entities. Here is the step by step procedure to do NER using spaCy:
CodeSignal
codesignal.com › learn › courses › linguistics-for-token-classification-in-spacy › lessons › unveiling-the-essentials-of-entity-recognition-with-spacy
Unveiling the Essentials of Entity Recognition with spaCy
Congratulations! You have learned the importance of Entity Recognition in NLP and implemented it efficiently using the spaCy library in Python. You have seen how we can process text and identify named entities, such as organizations, persons, and geographical locations, among others.
python - Can a Named Entity Recognition (NER) spaCy model or any code like an entity ruler around it catch my new further date patterns also as DATE entities? - Stack Overflow
Anonymization of entities found by a NER model I try to anonymize files by means of a NER model for German text that sometimes may have a few English words. If I take spaCy NER models for German and More on stackoverflow.com
[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, 2023python - Person Name Detection using SpaCy in English Lang. Looking for Answer - Stack Overflow
This is a typical Named Entity Recognition problem. Spacy has a pre-trained model to enable this, which should be accurate to detect person names. More on stackoverflow.com
Named Entity Recognition (NER) Libraries
For a supervised task like NER and these entities you’re likely going to have to build your own large dataset through manual work. But, i might suggest reframing the task and looking at question answering. Take a pretrained model thats been finetuned on squad, and for every doc ask things like ‘what are the materials used?”. Get a baseline and then if needed do further finetuning on your own data. Aka run the question on a hundred examples and curate them. Then finetune again. Note that this does require a llm and so limited access to compute might render this suggestion moot. But, I’ve seen success with this as a proxy for tricky ner for which I have little or no labelled data. More on reddit.com
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August 29, 2022Videos
25:12
Named Entity Recognition (NER) in Python: Pre-Trained & Custom ...
05:01
Best way to do Named Entity Recognition in 2024 with GliNER and ...
Named Entity Recognition (NER) using spaCy · spaCy Universe
spaCy's NER model · spaCy Universe
Named Entity Recognition (NER): NLP Tutorial For Beginners ...
29:59
Clinical Named Entity Recognition in Python with Spacy - YouTube
spaCy
spacy.io › api › entityrecognizer
EntityRecognizer · spaCy API Documentation
A transition-based named entity recognition component. The entity recognizer identifies non-overlapping labelled spans of tokens.
Factsheet
spaCy
Original author Matthew Honnibal
Developers Explosion AI, various
spaCy
Original author Matthew Honnibal
Developers Explosion AI, various
spaCy
spacy.io
spaCy · Industrial-strength Natural Language Processing in Python
Components for named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more
spaCy
spacy.io › usage › linguistic-features
Linguistic Features · spaCy Usage Documentation
spaCy is a free open-source library for Natural Language Processing in Python. It features NER, POS tagging, dependency parsing, word vectors and more.
Medium
medium.com › @sanskrutikhedkar09 › mastering-information-extraction-from-unstructured-text-a-deep-dive-into-named-entity-recognition-4aa2f664a453
Mastering Information Extraction from Unstructured Text: A Deep Dive into Named Entity Recognition with spaCy | by Sanskrutikhedkar | Medium
October 27, 2023 - These models, with their components ... our exploration into unstructured data. ... SpaCy is an open-source software library for advanced natural language processing (NLP) in Python....
FutureSmart AI
blog.futuresmart.ai › building-a-custom-ner-model-with-spacy-a-step-by-step-guide
Building a Custom NER Model with SpaCy: A Step-by-Step Guide
June 21, 2023 - By customizing the NER model using SpaCy, you can enhance its performance and achieve more accurate and context-specific named entity recognition. Importing the required libraries and downloading SpaCy models: import spacy !python -m spacy download en_core_web_lg nlp = spacy.load("en_core_web_lg")
Data Science Duniya
ashutoshtripathi.com › 2020 › 04 › 27 › named-entity-recognition-ner-using-spacy-nlp-part-4
Named Entity Recognition NER using spaCy | NLP | Part 4 – Data Science Duniya
November 16, 2021 - Spacy provides option to add arbitrary classes to entity recognition system and update the model to even include the new examples apart from already defined entities within model.
Analytics Vidhya
analyticsvidhya.com › home › named entity recognition (ner) in python with spacy
Named Entity Recognition (NER) in Python with Spacy
May 1, 2025 - NER using Spacy is the Python-based Natural Language Processing task that focuses on detecting and categorizing named entities.
Python Humanities
ner.pythonhumanities.com
INTRODUCTION TO NAMED ENTITY RECOGNITION ...
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Kaggle
kaggle.com › code › abhisarangan › ner-using-spacy
NER using Spacy
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spaCy
spacy.io › usage › spacy-101
spaCy 101: Everything you need to know · spaCy Usage Documentation
A trained pipeline can consist of multiple components that use a statistical model trained on labeled data. spaCy currently offers trained pipelines for a variety of languages, which can be installed as individual Python modules. Pipeline packages can differ in size, speed, memory usage, accuracy and the data they include. The package you choose always depends on your use case and the texts you’re working with. For a general-purpose use case, the small, default packages are always a good start. They typically include the following components: Binary weights for the part-of-speech tagger, dependency parser and named entity recognizer to predict those annotations in context.
Medium
medium.com › ubiai-nlp › fine-tuning-spacy-models-customizing-named-entity-recognition-for-domain-specific-data-3d17c5fc72ae
Fine-Tuning SpaCy Models: Customizing Named Entity Recognition for Domain-Specific Data | by Wiem Souai | UBIAI NLP | Medium
February 6, 2024 - As an open-source library, SpaCy provides pre-trained models for essential tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Its distinguishing features include exceptional speed and memory efficiency, enabling ...
Stack Overflow
stackoverflow.com › questions › 77700760 › can-a-named-entity-recognition-ner-spacy-model-or-any-code-like-an-entity-rule
python - Can a Named Entity Recognition (NER) spaCy model or any code like an entity ruler around it catch my new further date patterns also as DATE entities? - Stack Overflow
That is why this question here is hopefully not a duplicate: I cannot retrain the NER model since it is a ready-made download from spaCy. Mind that the answers there tell you not to ever add an entity ruler at all to the NER model if you can retrain your NER model since it may lead to "catastrophic forgetting" of the already trained NER model, read there for more. If that is right, I wonder what I am doing here at all since that would mean that I cannot merge the entity recognition that the spaCy NER model is trained on with another entity ruler.
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.
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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...
Sematext
sematext.com › home › blog › entity extraction with spacy
Entity Extraction with spaCy
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spacy has an label_ called person. you have several options for the model: small, medium or large. large uses more resources to run
def find_persons(text):
# Create Doc object
doc2 = nlp(text)
# Identify the persons
persons = [ent.text for ent in doc2.ents if ent.label_ == 'PERSON']
# Return persons
return persons
Try nltk to find the Nouns and then pattern match the nouns for valid names:
tokenized_sent = nltk.word_tokenize(sentence)
tagged_sent = nltk.pos_tag(tokenized_sent)
nouns
pronouns
adjectives
verbs
NNP - proper noun singular
PRP - proper noun
VB - verb
DT - determinant
NNP - proper noun singular
PRP - proper noun
VB - verb
DT - determinant
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This is a typical Named Entity Recognition problem. Spacy has a pre-trained model to enable this, which should be accurate to detect person names.
Take a look at this code sample.
According to Spacy's annotation scheme, names are marked as PERSON.
Analytics Vidhya
analyticsvidhya.com › home › custom named entity recognition using spacy v3
Custom Named Entity Recognition using spaCy v3 - Analytics Vidhya
October 14, 2024 - In this article, you will learn to develop custom named entity recognition which helps to train our custom NER pipeline using spacy v3.
Towards Data Science
towardsdatascience.com › home › latest › custom named entity recognition with bert
Named Entity Recognition NER using spaCy | NLP | Part 4
March 5, 2025 - If you think about it, solving ... most intuitive way to approach this task is to take the corresponding hidden state of each token and feed it through a classification layer....
Medium
josiah-adesola.medium.com › how-to-use-named-entity-recognition-in-spacy-to-analyze-blog-content-c46d50a8eb94
How to Use Named Entity Recognition in Spacy to Analyze Blog Content. | by Josiah Adesola | Medium
March 17, 2023 - We will be building the named entity recognition model using SpaCy (a Python library).