sdrabb/argumentation_mining_models

This repository provides all the models that we use to solve the Context Independent Claim Detection Argumentation Mining sub-task.

Argumentation Mining Models

This repository provides three types of Neural Networks that can be used to solve the Context Independent Claim Detection. We employ our models on the IBM dataset and for each of them we consider the pretrained word embeddings built with Glove model. For the Tree-LSTM model we follow the code of the Stanford Tree-Structured Long Short-Term Memory Networks.

Repository Structure

• LSTM implementation of the LSTM: the model is defined in the file lstm.py, scores.py is used to evaluate the model. The considered topics are listed in considered_topic.txt.

• RNN implementation of the RNN: the model is defined in the file rnn.py, scores_and_charts.py is used to evaluate the model.

• Tree-LSTM contains the changes made to the Tree-Structured Long Short-Term Memory Networks to fit their implementation to our task.

Built With

• Tensorflow - Long Short Term Memory and Recursive Neural Network
• Torch - Tree Structured Long Short Term Memory

References

• Argumentation Mining (https://dl.acm.org/citation.cfm?id=2850417)
• Tree-structured Long Short-Term Memory networks (https://arxiv.org/abs/1503.00075)

argumentation_mining_models's Issues

Regarding Datasets

Hey, @sdrabb this is Rishabh here. Actually I was working on IBM's argument mining dataset and their model. I find your lstm model really interesting. According to your readme file, this lstm model is trained on IBM Datasets.
Could you please share the datasets which you are using for score.py "data/arg_mining_new/topic_{}_test/topic_name.txt " . To what this link is referring too. I mean I am not getting your data/arg_mining file itself in the entire library.
Please do let me know as soon as possible.

Thank you.
Regards,
Rishabh Yadav.