This project contains up to 8 different Argument Mining Tools capable in predicting a sentence in English language as either argumentative or non-argumentative.
- pip install -r requirements.txt
- Python 2.7
The input requires text files located in src/data/original.
The application automatically generates a corresponding .csv file with output results for each input text file stored in src/data/updated.
Note that for input documents each line is assumed as one sentence.
Navigate to the directory src and run python Processor.py
There is a possibility to select N out of 8 models in property.txt.
Each sentence of an input document is predicted as majority vote of the N selected models.
Models with a "1" in usage field indicate as being selected and a "0" as non-selected.
The property file contains following models:
- Linear Discriminant Analysis (LDA)
- C-Support Vector Classification (SVM)
- Logistic Regression (LR)
- Random Forest (RF)
- AdaBoost classifier (ADA)
- K Nearest Neighbor (KNN)
- Gaussian Naive Bayes (GNB)
- Long short-term memory (LSTM)
We use the scikit-learn library for implementation of traditional Machine Learning classifiers.
In terms of LSTM we adapted the Neural Architectures for Named Entity Recognition described in Lample et al, 2016.
We use pre-trained word embeddings from Google News corpus in order to use this for LSTM application.
- Word2Vec: https://code.google.com/p/word2vec/
Insert the binary file in src package
- Alfred Sliwa (alfred.sliwa.92@stud.uni-due.de)
- Ahmet Aker (aker@is.inf.uni-due.de)
This project is licensed under the GNU LGPL - see the LICENSE.md file for details
If you use ArgMiningTools in your research, please cite the paper:
@inproceedings{aker2017works,
title={What works and what does not: Classifier and feature analysis for argument mining},
author={Aker, Ahmet and Sliwa, Alfred and Ma, Yuan and Lui, Ruishen and Borad, Niravkumar and Ziyaei, Seyedeh
and Ghobadi, Mina},
booktitle={Proceedings of the 4th Workshop on Argument Mining},
pages={91--96},
year={2017}
}