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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-

I would really appreciate help in this regard

4 Answers
4

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!

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

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.

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.

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