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I have set of technical sentences extracted from few research papers.

e.g., Via analyzing the peer feedback content, it was found that the feedback provided by the mixed mode group was of better quality than that provided by the "peer comments" group; that is, the former provided more detailed feedback to individuals than the latter.

I want to extract the entities in the sentences and the relationships that take part with the identified. (e.g., SOV relationships)

What are the tools that I can use to efficiently extract them?

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  • $\begingroup$ Can you give a example of a sentence so that it will clarify exactly which type of entities you are talking about ? $\endgroup$
    – Abhishek Verma
    Commented Jun 26, 2017 at 18:06
  • $\begingroup$ Thank you for the comment. Example sentence is like this. E.g., Via analyzing the peer feedback content, it was found that the feedback provided by the mixed mode group was of better quality than that provided by the "peer comments" group; that is, the former provided more detailed feedback to individuals than the latter. I want to come up with SOV something like <feedback> <analyze> <mixed mode group> $\endgroup$
    – Smith
    Commented Jun 26, 2017 at 23:25

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Open Calais is a free-to-use tool for entity recognition and relationship mapping. It's from Thompson-Reuters so may not be wholly suitable for technical language but worth a try. Has python bindings

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  • $\begingroup$ Thank you for the reply. But as you have said it doesn't capture technical entities. $\endgroup$
    – Smith
    Commented Jun 27, 2017 at 6:19

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