I have been using the Stanford NER tagger to find the named entities in a document. The problem that I am facing is described below:-
Let the sentence be The film is directed by Ryan Fleck-Anna Boden pair.
Now the NER tagger marks Ryan as one entity, Fleck-Anna as another and Boden as a third entity. The correct marking should be Ryan Fleck as one and Anna Boden as another.
Is this a problem of the NER tagger and if it is then can it be handled?
How about
take your data and run it through Stanford NER or some other NER.
look at the results and find all the mistakes
correctly tag the incorrect results and feed them back into your NER.
lather, rinse, repeat...
This is a sort of manual boosting technique. But your NER probably won't learn too much this way.
In this case it looks like there is a new feature, hyphenated names, the the NER needs to learn about. Why not make up a bunch of hyphenated names, put them in some text, and tag them and train your NER on that?
You should get there by adding more features, more data and training.
Instead of using stanford-coreNLP you could try Apache opeNLP. There is option available to train your model based on your training data. As this model is dependent on the names supplied by you, it able to detect names of your interest.
Related
I have trained Stanford NER to extract the organization names from text. I used IO tagging format. It works fine. However, I wonder if changing the tag format to IOB (or other formats) might improve the scores. ?
Suppose you have a sentence that lacks normal punctuation, like this:
John Sam Ted are all here.
If you don't have a B tag you won't be able to tell if this should be three entities or one entity with three words.
On the other hand, for many common types of entities, they can't just run together in normal English text since you'll at least have a comma between them.
If you can set it up, using IOB is better in case you have entities run together, but depending on your data set it may not be an issue. You'll have to look at the data to tell.
We would like to add some custom entities to the training set of either Stanford NLP or spaCy, before re-training the model. We are willing to label our custom entities, but we would like to add these to the existing training set, so as to not spend too much time labeling.
We assume that the NLP model was trained on a large labeled data set, which includes labels for words that are labeled "O" ("other", i.e. nothing of interest) as well as words that are labeled "DATE", "PERSON", "ORGANIZATION", etc. We have a custom set of ORGANIZATION words, but we would like to add these to all the other labeled data, before re-training the model.
Is this possible? How can we do this? Do we have to get the labeled dataset that the models were trained on, so we can add our own data? If so, how can we do that?
We have built prototypes using both Stanford NLP and spaCy, so an answer for either one works for us.
For spaCy, you should just be able to call nlp.update(). This will make a weight update against the current weights, allowing you to resume training. If you want to make many updates, you might want to parse some text with the original model and mix that through your training, to avoid the "catastrophic forgetting" problem.
You can use this entity tagger tool by helkaroui to create your own training set.
I used NER of StanfordCoreNLP to recognize the entity including organization, location and person. But there exists something weird. For example, I input a sentence like "Cleveland Cavaliers" and it will recognize the 'Cleveland' as 'location' but not 'Cleveland Cavaliers' as organization.
I am not very familiar with the ner and I don't know how the NER works. My task is to get all the company name in the text and the result I have got is not very satisfactory. So there are two ways occuring to me to solve the problem. The first is to modify the dict and insert the correct data. The second is to train the model. But there are still some questions.
Will the first way work effectively?
If the answer of question 1 is yes, how to modify the dict?
Further more, the FAQ list at https://nlp.stanford.edu/software/crf-faq.shtml#a proposed the way to train the ner model but what confused me most is what I will get if I trained my model.
If I create a dataset containing like
"organization 'Cleveland
Cavaliers'"
to train the model, what will happen in the model? The dict inside the CRFClassifier will change?
Will the CRFClassifier modify the bug when I input 'Cleveland Cavaliers' and recognize the 'Cleveland Cavaliers' as an organization entity?
These are all my puzzles and I am preparing the dataset to try the second way. Can anybody answer the 4 questions above?
Thanks
I think the first solution is not very technical and every time you want to tag a new company, you need to update your dictionary.
I prefer your second solution and I do this before and trained a new model to tag my sentences.
If you have a good corpus that is big enough which tagged properly, It may take some time to train, but it worth the effort.
I know how to train an NER model as specified here and have a very successful one in fact. I also know about the 3 provided caseless models as talked about here. But what if I want to train my own caseless model, what is the trick there? I have a bunch of all uppercase documents for training. Do I use the same training process or are there special/different features for the caseless models or are there properties that need to be set? I can't find a description as to how the provided caseless models were created.
There is only one property change in our models, which is that you want to have it invoke a function that removes case information before words are processed for classification. We do that with this property value (which also maps some words to American spelling):
wordFunction = edu.stanford.nlp.process.LowercaseAndAmericanizeFunction
but there is also simply:
wordFunction = edu.stanford.nlp.process.LowercaseFunction
Having more automatic stuff for deciding document format (hard/soft line breaks), case, or even language would be nice, but at present we don't have any of those....
I haven't found anything in the documentation about adding more tagged words to the tagger, specifically the bi-directional one.
Thanks
At present, you can't. Model training is an all-at-one-time operation. (Since the tagger uses weights that take into account contexts and frequencies, it isn't trivial to add new words to it post hoc.)
There is a workaround. It is ugly but should do the trick:
build a list of "your" words
scan text for these words
if any matches found to POS tagging yourself (NLTK can help you here)
feed it to Stanford parser.
FROM: http://www.cs.ucf.edu/courses/cap5636/fall2011/nltk.pdf
"You can also give it POS tagged text; the parser will try to use
your tags if they make sense.
You might want to do this if the parser makes tagging
mistakes in your text domain."