Multitask Models for Supervised Protest Detection in Texts

EasyChair Preprint no. 1222

Abstract

The CLEF 2019 ProtestNews Lab tasks participants to identify text relating to political protests within larger corpora of news data. Three tasks include article classification, sentence detection, and event extraction. I apply multitask neural networks capable of producing predictions for two and three of these tasks simultaneously. The multitask framework allows the model to learn relevant features from the training data of all three tasks. This paper demonstrates performance near or above the reported state-of-the-art for automated political event coding though noted differences in research design make direct comparisons difficult.

Keyphrases: automated event data pipeline, event data, machine learning, neural networks, political event data, political protests, protest detection, Recurrent Neural Networks, semantic role labeling task, text data

@Booklet{EasyChair:1222,
  author = {Benjamin Radford},
  title = {Multitask Models for Supervised Protest Detection in Texts},
  howpublished = {EasyChair Preprint no. 1222},

  year = {EasyChair, 2019}}