OpenNLP vs Stanford CoreNLP

Question

I\'ve been doing a little comparison of these two packages and am not sure which direction to go in. What I am looking for briefly is:

1. Named Entity Recognition (pe

Answer 1

A bit late here, but I recently looking at OpenNLP based just on the fact that Stanford is GPL licenced - if thats ok for your project then Stanford is often referred to as the benchmark/state-of-the-art for NLP.

That said, the performance for the pre-trained models will depend on your target text as it is very domain specific. If your target text is similar to the data that the models were trained against then you should get decent results, but if not then you will have to train the models yourself and it will depend on the training data.

A strength of OpenNlp it that it is very extensible and is written for easy use with other libraries and has a good API for integrating - the training is very simple (once you have your training data) with OpenNLP (I wrote about it here - with a pretty lousy generated data set I was able to get ok results identifying foods), and it is very configurable - you can configure all the parameters around training very easily and there are a range of algorithms you can use (perceptron, max entropy, and in the snapshot version they have added Naive Bayes)

If you find that you do need to train the models yourself, I would consider trying out OpenNlp and seeing how it performs just for comparison, as with fine tuning you can get pretty decent results.