Improve CoreNLP POS tagger and NER tagger? The CoreNLP parts of speech tagger and name entity recognition tagger are pretty good out of the box, but I'd like to improve the accuracy further so that the overall program runs better. To explain more about accuracy -- there are situations in which the POS/NER is wrongly tagged. For instance:


"Oversaw car manufacturing" gets tagged as NNP-NN-NN


Rather than VB* or something similar, since it's a verb-like phrase (I'm not a linguist, so take this with a grain of salt).

So what's the best way to accomplish accuracy improvement?


Are there better models out there for POS/NER that can be incorporated into CoreNLP?
Should I switch to other NLP tools?
Or create training models with exception rules?
Your best best is to train your own models on the kind of data you're going to be working with.