CODEWITHSUNDEEP
pythonnlpnltknamed-entity-recognition
user1502248
user1502248

Reputation: 501

NLTK Named Entity Recognition with Custom Data

I'm trying to extract named entities from my text using NLTK. I find that NLTK NER is not very accurate for my purpose and I want to add some more tags of my own as well. I've been trying to find a way to train my own NER, but I don't seem to be able to find the right resources. I have a couple of questions regarding NLTK-

  1. Can I use my own data to train an Named Entity Recognizer in NLTK?
  2. If I can train using my own data, is the named_entity.py the file to be modified?
  3. Does the input file format have to be in IOB eg. Eric NNP B-PERSON ?
  4. Are there any resources - apart from the nltk cookbook and nlp with python that I can use?

I would really appreciate help in this regard

Upvotes: 50

Views: 29908

Answers (6)

iEriii
iEriii

Reputation: 401

To complete the answer by @Thang M. Pham, you need to label your data before training. To do so, you can use the spacy-annotator.

Here is an example taken from another answer: Train Spacy NER on Indian Names

Upvotes: 0

Thang Pham
Thang Pham

Reputation: 1026

  1. Are there any resources - apart from the nltk cookbook and nlp with python that I can use?

You can consider using spaCy to train your own custom data for NER task. Here is an example from this thread to train a model on a custom training set to detect a new entity ANIMAL. The code was fixed and updated for easier reading.

import random
import spacy
from spacy.training import Example

LABEL = 'ANIMAL'
TRAIN_DATA = [
    ("Horses are too tall and they pretend to care about your feelings", {'entities': [(0, 6, LABEL)]}),
    ("Do they bite?", {'entities': []}),
    ("horses are too tall and they pretend to care about your feelings", {'entities': [(0, 6, LABEL)]}),
    ("horses pretend to care about your feelings", {'entities': [(0, 6, LABEL)]}),
    ("they pretend to care about your feelings, those horses", {'entities': [(48, 54, LABEL)]}),
    ("horses?", {'entities': [(0, 6, LABEL)]})
]
nlp = spacy.load('en_core_web_sm')  # load existing spaCy model
ner = nlp.get_pipe('ner')
ner.add_label(LABEL)

optimizer = nlp.create_optimizer()

# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "ner"]
with nlp.disable_pipes(*other_pipes):  # only train NER
    for itn in range(20):
        random.shuffle(TRAIN_DATA)
        losses = {}
        for text, annotations in TRAIN_DATA:
            doc = nlp.make_doc(text)
            example = Example.from_dict(doc, annotations)
            nlp.update([example], drop=0.35, sgd=optimizer, losses=losses)
        print(losses)

# test the trained model
test_text = 'Do you like horses?'
doc = nlp(test_text)
print("Entities in '%s'" % test_text)
for ent in doc.ents:
    print(ent.label_, " -- ", ent.text)

Here are the outputs:

{'ner': 9.60289144264557}
{'ner': 8.875474230820478}
{'ner': 6.370401408220459}
{'ner': 6.687456469517201}
... 
{'ner': 1.3796682589133492e-05}
{'ner': 1.7709562613218738e-05}

Entities in 'Do you like horses?'
ANIMAL  --  horses

Upvotes: 0

xji
xji

Reputation: 8277

There are some functions in the nltk.chunk.named_entity module that train a NER tagger. However, they were specifically written for ACE corpus and not totally cleaned up, so one will need to write their own training procedures with those as a reference.

There are also two relatively recent guides (1 2) online detailing the process of using NLTK to train the GMB corpus.

However, as mentioned in answers above, now that many tools are available, one really should not need to resort to NLTK if streamlined training process is desired. Toolkits such as CoreNLP and spaCy do a much better job. As using NLTK is not that much different to writing your own training code from scratch, there is not that much value in doing so. NLTK and OpenNLP can be regarded as somehow belonging to a past era before the explosion of recent progress in NLP.

Upvotes: 1

arop
arop

Reputation: 451

I also had this issue, but I managed to work it out. You can use your own training data. I documented the main requirements/steps for this in my github repository.

I used NLTK-trainer, so basicly you have to get the training data in the right format (token NNP B-tag), and run the training script. Check my repository for more info.

Upvotes: 1

Rohan Amrute
Rohan Amrute

Reputation: 774

You can easily use the Stanford NER alongwith nltk. The python script is like

from nltk.tag.stanford import NERTagger
import os
java_path = "/Java/jdk1.8.0_45/bin/java.exe"
os.environ['JAVAHOME'] = java_path
st = NERTagger('../ner-model.ser.gz','../stanford-ner.jar')
tagging = st.tag(text.split())

To train your own data and to create a model you can refer to the first question on Stanford NER FAQ.

The link is http://nlp.stanford.edu/software/crf-faq.shtml

Upvotes: 14

jjdubs
jjdubs

Reputation: 316

Are you committed to using NLTK/Python? I ran into the same problems as you, and had much better results using Stanford's named-entity recognizer: http://nlp.stanford.edu/software/CRF-NER.shtml. The process for training the classifier using your own data is very well-documented in the FAQ.

If you really need to use NLTK, I'd hit up the mailing list for some advice from other users: http://groups.google.com/group/nltk-users.

Hope this helps!

Upvotes: 24

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