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.

Filter by
Sorted by
Tagged with
0
votes
1answer
18 views

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 ...
0
votes
0answers
6 views

NER tagging schema for non contiguous tokens

Looking at NER tagging, it seems that the most common tagging procedure is IOB. But it seems that this kind of tagging is limited to cases where tokens from the ...
0
votes
0answers
10 views

Tokenization with charachter offsets for NER

I'm working on NER, and have labeled data with char offsets. Is it possible to somehow finetune a tokenizer so that it'll be compatible with the char offsets? I.e., a token is either inside the offset ...
0
votes
0answers
10 views

Extracting entities from Job Description (Each entity vs Combined Entity)?

I have corpus of job descriptions in raw text where each JD is appended as single long text. The ideal goal is to extract entities (like, skills, education, job title, certifications (if any)), I have ...
0
votes
0answers
77 views

Named Entity Recognition (NER) Evaluation Metrics

Any one has an idea about the difference between CoNLL 2003 and Semeval 2013 metrics for named entities. In fact, my question is in the strict matching ,should we have the same performance with the ...
0
votes
0answers
43 views

BERT - CRF for named entity recognition

I am working on named entity recognition and I am using BERT-CRF model. I am asking if enriching the model with POS and Chunk features can improve the model performance. If it is not the case, are ...
0
votes
0answers
11 views

Annotating Dataset for Named Entity Recognition

I have New data of Plants research. It has the product it gives, the fertilizers that should be used for the plant and many more.. The names of plants,products and everything are unique and none of ...
0
votes
0answers
5 views

Which model should be best fit for detecting class of a context for specific entity?

Let's say I have a context which have sentence like "PersonX left the companya and joined companyb as a new CEO". In this sentence, I developed NER to detect the company names. So having the company ...
1
vote
1answer
41 views

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 ...
0
votes
1answer
95 views

Named Entity Recognition with Bert on very long Italian documents

As the title suggests, I'm wondering if it's feasible to use Bert to solve the Entity Named Recognition task on long legal documents (> 50.000 chars) in Italian. Now I'm using Spacy, and I'm ...
3
votes
1answer
1k views

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 ...
0
votes
1answer
201 views

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?
0
votes
0answers
45 views

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 ...
0
votes
1answer
103 views

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 ...
0
votes
1answer
60 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 ...
1
vote
1answer
773 views

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 ...
1
vote
0answers
96 views

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 ...
1
vote
0answers
24 views

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 ...
1
vote
0answers
11 views

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 ...
0
votes
1answer
79 views

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 ...
1
vote
0answers
107 views

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 ...
0
votes
1answer
62 views

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 ...
2
votes
1answer
754 views

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 ...
2
votes
1answer
300 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 ...
3
votes
1answer
865 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, ...
5
votes
1answer
798 views

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 ...