CoreNLP is a very good baseline for any NLP work. If you can build something that beats it on a specific benchmark it's a pretty good bet you have something that's pretty close to the state-of-the-art.
But.. there are problems. As software engineers, the (many) authors make great researchers.
CoreNLP is wonderful in the many different ways it almost lets you integrate into it without hacking the code. Changing the config of the various components is fantastic, because you get a very comprehensive view of many different ways people can configure almost the same thing. Environment variables? System properties? Properties files in a specific location? In the classpath? Json config? YAML? It almost supports them all - or rather different parts use different ones, and the only way to work out exactly how it works is to read the code.
Also, the licensing is annoying. Everyone doing commercial stuff with it just puts it behind a web service anyway, so they should just embrace non-viral license and get some input from the community.
Also SUTime. Yes, it works mostly, but wow :(
(Sorry for the rantish post. I use parts of CoreNLP a lot, and I'd love to see it improve.)
Have you used spacy.io for comparison? The author seems to enjoy trolling about the faults of CoreNLP, but the license is very agreeable and the results seem on par.
I've looked at it, and played with a bit. I think when I last looked at it the licensing was even less friendly than CoreNLP. I believe this has been fixed though, so it's probably worth looking at again.
From memory when I was last looking around I cared mostly about named entity recognition (NER) and Spacy themselves say CoreNLP is better. CoreNLP has more features too.
WordVector integration in Spacy looks interesting though. That's probably enough to make me have a play with it.
Spacy is really fast, the author is extremely knowledgeable, and it works well on the datasets it was trained on. Problem with SpaCy for me was that it was pre-trained on those texts and it was not possible to train it on new things. Also their parser wasn't very customizable.
This pre-trained model is borderline useless if you want to obtain good results on your data, which is probably very different from the data they trained on.
For NER in the Python world, the best option is pycrfsuite. It works really quickly and lets you easily define your own features. CRFSuite itself is a work of art. I only wish Okazaki incorporated 2nd order transitions because that makes a huge difference on some datasets.
CoreNLP is a lot worse than pyCRFsuite in terms of ease of integration and performance in my experience. Particularly, if you want to define your own features.
Your Word2Vec comments are interesting. I've had pretty good success with that original trained dataset the original Word2Vec implementation shipped with. That's a big dataset of course, but one of the strengths of the model is that if your training dataset is big enough you don't really need a lot of specialized training.
CRFSuite
I've never used this, but it's not really a ready-to-use NLP toolkit is it? Isn't it more a tool for building NLP tools with?
Gensim's implemention is better than the original and allows for Python API access to all the features. Highly recommended. Radim tends to write really memory efficient code unlike some other Python libs, so you can deal with large datasets.
If you want to do NER in a way that doesn't suck there is no way around making your own model on your own training data.
It honestly takes only a few days of labeling things yourself. I found that outsourcing the work to amazon turk is not a viable option because the graders there are terrible. And they work about 30x slower than you do. Even if you pay them $1/hr, that is like paying one person $30/hr. I'm not kidding.
Sure you can do a quick and dirty "send data to these guys and they'll do all the work", but I haven't come across a model that works well on all datasets. We're talking going from 30ish percent accuracy for a model not trained on your dataset to low 90s for something trained on your dataset. Of course, these are approximate numbers and it is definitely possible that your dataset is almost exactly like the ones they trained their model on.
3. Do more labeling, retokenizing, neural embedding from word2vec's similar words to the tokens you have, tune parameters or come up with better features such as your own dictionaries of entities, etc. Retrain the model.
#2 weeks.
4.Done. Now you have a memory efficient fast model tuned on your data. You can label anything you want. Not just Person/Company, but things like car vs bicycle brands, computer parts, obfuscated email addresses, etc.
I want to make "domain adaptation as a service" the key part of spaCy's business model: you send us text, we send you a good model. Internally this will probably involve annotating part of the text, but that's a tactical decision we'll make.
I hope we can make some break-throughs that help NER be much more general than it is currently. But the current solution you describe works fine; it's just a pain in the ass for each organization to take on. We want to have the required infrastructure and expertise set up, and make the process seamless.
But the code and config mechanisms are terrible to use and the documentation of them is even worse.
It's ok if you want to use it as it is out of the box. But try to do something like change to forward looking dates ("On Monday" should mean next Monday instead of last Monday) and it isn't as easy as it should be!
Ok, yes, you're absolutely right, thanks for responding. I have customized it a bit (e.g. changing "yesterday" to return a TIMEX3 Date instead of a date/time, roughly speaking, I'm probably forgetting the details). I was really hoping you knew of something better ;)
Originally I got interested because of an essay PG wrote about Bayesnian spam filtering, so I wrote a Bayesian classifier in Java (this was over 10 years ago, when that was pretty cutting edge).
That led to text summarisation - still open source Java stuff, and apparently now considered state-of-the-art[1] (I'm kind of amazed, because that code is 10 years old).
Then I did some AdTech stuff, wrote an open domain natural language question answering thing (like Watson, but not as good - but it was just for fun. bAIb is the way to approach this now if anyone is interested).
Now-days I'm using NLP for future event prediction.
But.. there are problems. As software engineers, the (many) authors make great researchers.
CoreNLP is wonderful in the many different ways it almost lets you integrate into it without hacking the code. Changing the config of the various components is fantastic, because you get a very comprehensive view of many different ways people can configure almost the same thing. Environment variables? System properties? Properties files in a specific location? In the classpath? Json config? YAML? It almost supports them all - or rather different parts use different ones, and the only way to work out exactly how it works is to read the code.
Also, the licensing is annoying. Everyone doing commercial stuff with it just puts it behind a web service anyway, so they should just embrace non-viral license and get some input from the community.
Also SUTime. Yes, it works mostly, but wow :(
(Sorry for the rantish post. I use parts of CoreNLP a lot, and I'd love to see it improve.)