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Improving Sentiment Analysis with Multi-task Learning of Negation

Python 60.42% Perl 39.58%
multi-task sentiment-analysis nlp negation-scope

multitask_negation_for_sa's People

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multitask_negation_for_sa's Issues

Prediction results

Hi!

Thank you for making the code available!
I have a question regarding the hierarchical_training model. I'm currently running it and I observed that the file only contains the train_model function. For getting the results (precision, recall, f1) do I have to implement another function or is there any other file that I didn't see and already contains the implementation?

Thank you for your time.
Best regards.

Usage notes

Thanks for the very insightful publication and for making the code available here! Two minor things/questions:

  • I couldn't quite figure out which embeddings were used for training/validating. The model/training both refer to "../../embeddings/google.txt" as a default for the embeddings and I used Google News 2013 from here which seems to work fine. Could you maybe clarify what exactly was used? And sorry if this was mentioned somewhere and I just missed it.
  • In addition to the dependencies mentioned in the README (scikit-learn, torch, torchvision and nltk) I also had to explicitly install (i.e. those weren't pulled as requirements for something else) the following in a fresh environment to get everything up and running: matplotlib, tqdm and torchtext.

Thanks again!

Christoph

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