<p>This is quite a broad question. There are many ways you could build a new OpenIE model.</p>
<p>Are you trying to build a rule based model? A deep learning model? A combination of both? What aspect of Stanford CoreNLP's OpenIE annotator do you want to improve?</p>
<p>It's quite difficult to answer this question without your level of domain knowledge, deep learning skills, etc. I suggest you first review a survey on open information extraction, like this one: <a href="https://arxiv.org/abs/1806.05599" rel="nofollow noreferrer">https://arxiv.org/abs/1806.05599</a>, and decide on an architecture.</p>
<p>Then, you could build your own supplementary training set, or a dataset from scratch that suits your needs, which can be used to train an already existing architecture.
Of course, you are free to modify the architecture as well. If you feel a rule-based model would suffice, you could either edit the appropriate code, or post-process results from a rule-based model.</p>
How to train a new OpenIE model?
<p>I am using stanford-corenlp-4.2.0 for extracting data from unstructured text. It seems OpenIE is helpful but should be improved for my specific scenario.
Is it possible to train a new openie model and how?
Thanks</p>