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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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NER with GRUs neural network with imbalance dataset

this is my first time asking question on CrossValidated, so if there is any mistake on my part, i apologize. I will try not to make those mistakes again. I'm trying to do a NER task. The problem is ...
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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?
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Visualize Named Entities Relations

I have a dataset of named entities (NER) and text documents. For each entity I have N documents associated, while an entity belongs to one among multiple classes like (here the CoreNLP ones) ...
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Named Entity Recognizer for nominal phrases: Airbus vs Europe's biggest airplane manufacturer

i'm looking for a Named Entity Recognizer that is able to recognize "true names" as well as phrases that are synonymous for them. If possible i would like to use python. Can NLTK or spaCy models be ...
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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 ...
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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 ...
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Supervised ML opinion mining of medical entities: tools?

I would like to extract relations between the entities of 'quality of life' and 'health interventions' out of a corpus of medical texts via supervised machine learning. I wonder whether there is a ...
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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 ...
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What does the OffsetConjunction method in the MALLET machine learning toolkit do?

Anyone having built sequence learning models using the MALLET toolkit and had used the OffsetConjunction method for features, could kindly provide an idea of what the method does exactly. An example ...
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Conditional Random Fields: model criticism

When decoding a sequence using the Viterbi algorithm for Conditional Random Fields (CRF), we obtain the most probable class given the complete sequence. What would be possible ways to visualize errors ...
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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 ...
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Identifying whether a name is mutual fund or not? [closed]

I have names of ~9000 mutual fund. Given a new name, I wish to classify whether it is a mutual fund or not. I don't have many examples of non-mutual fund names (like hedge funds, exchange traded funds ...
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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 ...
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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 ...
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Named Entity Recognition using the surrounding describing words

I'm trying to put Proper Nouns into buckets of "NAME", "LOCATION", "ORGANIZATION" using the words around these words. For ex - "His name is John" , John has the describing word of "His" while "Their ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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, ...
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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 ...