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I am looking for a way to extract the potential subject and object from a question in French.

For the moment, I am building some handmade rules. Alternatively, I started to think of using an already trained model for the task. I wanted to use StanfordNLP parser but unfortunately it is not free for commercial use.

So, here comes the questions:

  1. Are they ML models used for this task?

  2. Are they labeled data sets (in french) which can help create ML models used for this task?

[Edit] I am looking mainly for open source tools/ libraries.

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  • $\begingroup$ Have you seen this post: stackoverflow.com/questions/37297399/…? I would try the same thing for French. Spacy has support for French as well spacy.io/usage/models. I'd recommend the "fr_core_news_md" mode rather the small model. $\endgroup$ Nov 30, 2018 at 9:38
  • $\begingroup$ I don't think that spacy's parser can be used for french : spacy.io/api/annotation#section-dependency-parsing $\endgroup$ Nov 30, 2018 at 10:27
  • $\begingroup$ Consider also using Google's Cloud Natural Language. For testing use the text box under Try the API. For example, I tried "Qu'est-ce qu'il lui fait?" and the Syntax analysis resulted in telling me that "qu'" is dobj, "il" is nsubj, and "lui" is nobj. I do not speak French so I can't help you further with this :) $\endgroup$
    – mapto
    Nov 30, 2018 at 15:07
  • $\begingroup$ Thank you for the suggestion but I am looking for open source libraries. $\endgroup$ Nov 30, 2018 at 16:29
  • $\begingroup$ Did you check for dependency parsing? $\endgroup$ Dec 5, 2018 at 4:47

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Google SyntaxNe has pre-trained models for French. It is open source and can be customized for a specific use-case.

https://github.com/tensorflow/models/blob/master/research/syntaxnet/g3doc/universal.md

With this, you can create a dependency tree for questions and evaluate fitness for this application.

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  • $\begingroup$ Does it support python or is it only usable via command lines? $\endgroup$ Dec 19, 2018 at 8:53
  • $\begingroup$ Command line examples are for demo, it is meant to be used from Python or other languages that interface with Tensorflow. $\endgroup$ Dec 25, 2018 at 4:26

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