AMI-EVALITA2018
Code for replicating results of team 'hateminers' ( now hate-alert) at EVALITA-2018 for 'Automatic Misogyny Identification' task. We have explained the model details in our paper ''Hateminers : Detecting Hate speech against Women''. We came First for the English Subtask A and Fifth for the English Subtask B.
Please cite our paper in any published work that uses our model.
Saha, P., Mathew, B., Goyal, P. & Mukherjee, A., (2018). Hateminers: Detecting Hate speech against Women. arXiv preprint arXiv:1812.06700.
@article{saha2018hateminers,
title={Hateminers: Detecting Hate speech against Women},
author={Saha, Punyajoy and Mathew, Binny and Goyal, Pawan and Mukherjee, Animesh},
journal={arXiv preprint arXiv:1812.06700},
year={2018}
}
Directory structure
root
|
|-----AMI_data (data needs to be kept here)
|
|-----Classifier
|
|-----taskA(models and feature selection methods for task A- misogynous or not)
|
|-----taskB1(models and features selection methods for task B1- misogyny category )
|
|-----taskB2(models and features selection methods for task B2- misogyny category )
Predicitons
In order to make the predicition feature vector consisting tfidf-vectors , glove embeddings , google-universal-encoding should be prepared.
clf_task1=joblib.load('taskA/'+model_for_taskA)
clf_task2=joblib.load('taskB1/'+model_for_taskB1)
clf_task3=joblib.load('taskB2/'+model_for_taskB2)
select_task1=joblib.load('taskA/'+select_featue_for_taskA)
select_task2=joblib.load('taskB1/'+select_featue_for_taskB1)
select_task3=joblib.load('taskB2/'+select_featue_for_taskB2)
Assuming x to be a feature vector.Models of Task B are only used when Task A model predicts the given text as misogynous.
x=x.reshape(1,-1)
temp=x
temp=select_task1.transform(temp)
predict1=clf_task1.predict(temp)
if(int(predict1[0])!=0):
temp1=x
temp1=select_task2.transform(temp1)
predict2=clf_task2.predict(temp1)
temp2=x
temp2=select_task3.transform(temp2)
predict3=clf_task3.predict(temp2)
else:
list_2.append(0)
list_3.append(0)
More details can be found in here
License
This project is licensed under the MIT License - see the LICENSE.md file for details