Questions tagged [named-entity-recognition]

Named-entity recognition (NER) (also known as entity identification, and entity extraction) is a subtask of information extraction that seeks to locate and classify elements in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.

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What is a good approach to annotate invoice numbers for NER?

I have a task for Document object detection. From bank reasons I need to extract several things. One of these things are invoice numbers. The issue with invoice numbers is that one reason can contain ...
Yana's user avatar
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Finding a tag / multiple tags across multiple documents

Suppose we have text documents: ...
Wiliam's user avatar
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BIO vs. BIEWO tagging scheme

In one of my classes, the lecturer explains the tagging scheme BIO (begin, inside, outside) which I knew of and I perceive as the standard. Then he introduced BIEWO which additionally has a tag for ...
Marcel Braasch's user avatar
2 votes
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415 views

Is NER suitable for selecting an entire sentence as an entity?

If I have a document with many paragraphs of text, would using a custom NER model be suitable to identify a sentence as a recognized entity? The desired sentences will be similar in semantic structure-...
Jason p's user avatar
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3 votes
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Best practice for named entity recognition on large texts

What are the best practices to apply NER to large texts (e.g 20 pages+)? One common advice is to split the text before passing it as input to the model. However this can require a significant manual ...
mobupu's user avatar
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2 answers
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Named entity recognition with only one pure entity(no context)?

We know that we can extract entities from a sentence using named entity recognition, but what if the sentence contains only an entity and no other context? For example, we can use CRF for the ...
Lerner Zhang's user avatar
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5 votes
2 answers
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Evaluation metric for named entity recognition

I was watching Manning's lecture on evaluation of NER models, and I'm confused why between 4:07 and 5:13, he states that the error in not labeling one word in a sequence while correctly labeling the ...
Vivek Subramanian's user avatar
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While evaluating a named entity recognition system, why does partial matching seem to be important?

Through some internet sources, I read that partial matching of classes is also important for finding the precision and recall of a Named entity system. Why is it so?
Chanchal Suman's user avatar
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Approach for differentiating "PEOPLE" in Named Entity Recognition

I am using Spacy's pertained model to identify people in the IMDB movie reviews dataset. While the model identifies people in the reviews, I want to find out people who are characters of the movie as ...
ajit samudrala's user avatar
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Does Alphabet Case Matters while Training Stanford Open NLP NER classifier?

I'm working with Named Entity Recognition for non-English. I've some raw text file (all small letter) and trying to make NER classifier. I'm not sure If it'll be better using Small Capital mixed text ...
gamerrishad's user avatar
1 vote
1 answer
164 views

Referring multiple names to the same entity

I am working on the models of different product types and wish to generalize them to the same entity. For example, from the given list ...
user_01's user avatar
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NER at sentence level or document level?

Should NER models (LSTM or CRF) take input training data at sentence level or paragraph level? Let's say we have this input text, and we would like to do Named Entity Extraction: GOP Sen. Rand ...
Frank's user avatar
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Identifying locations in a difficult OCR-read English text with python

My goal is to identify city and (US) state of both inventor and assignee of US patents from the 1910's and 1920's. These patents are provided by google and look like so, like so or like so. The ...
MERose's user avatar
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Sequence Tagging with Incomplete Labels

I have a few thousands of sentences with their B/I/O NER tags. I also have access to millions of sentences for which only some words are tagged. That is, for the other words, I don't know whether they ...
mossaab's user avatar
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Ranking Text Posts based on NER Tags

I have a list of NER tags ranked by the number of times they appear in a database of articles, with the highest count listed first. I am trying to figure out how to score the articles based on: How ...
David Kobia's user avatar
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1 answer
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Calculating the accuracy of a machine learning system in runtime

I have a question in mind about machine learning systems. Say, I have a named entity extraction systems which give me an accuracy of 90%(precision 90, and recall 90) during training and testing. How ...
Kumaresp's user avatar
1 vote
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latent dirichlet allocation for name disambiguation

I'm trying to implement latent dirichlet allocation on a name disambiguation project. My data set includes a corpus of documents. Each document looks like: Author, co-author, title, institution I ...
casualprogrammer's user avatar
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Convolutional Neural Networks for identification of specific objects instead of object categories?

Is it for example feasible to hand a query image of my dog "Dave" to a CNN, and then use the CNN to find all images of "Dave" in a database of dog images? If so, how would one do this? If not, why ...
MLenthousiast's user avatar
2 votes
1 answer
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Named entity recognition (NER) features

I'm new to Named Entity Recognition and I'm having some trouble understanding what/how features are used for this task. Some papers I've read so far mention features used, but don't really explain ...
Mr. Phil's user avatar
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2 votes
1 answer
375 views

Is there any advantage of using MEMM instead of CRF for named-entity recognition?

I wonder whether there is any advantage of using maximum-entropy Markov model (MEMM), a.k.a. conditional Markov model (CMM) instead of using conditional random fields (CRF) for named-entity ...
Franck Dernoncourt's user avatar
3 votes
1 answer
924 views

Reinforcement Learning & Text Mining

I was wondering if one could use Reinforcement Learning (as it is going to be more and more trendy with the DeepMind & AlphaGo's stuff) to parse and extract information from text. For example, ...
mic's user avatar
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5 votes
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Sequence length when training a conditional random field (CRF)

I am training a conditional random field (CRF) to perform named entity recognition. I have 1000 documents, each containing from 100 to 500 sentences. During the training phase, is it better to train ...
Franck Dernoncourt's user avatar