Anindita Sinha Banerjee


Weakly Supervised Extraction of Tasks from Text
Sachin Pawar | Girish Palshikar | Anindita Sinha Banerjee
Proceedings of the 18th International Conference on Natural Language Processing (ICON)

In this paper, we propose a novel problem of automatic extraction of tasks from text. A task is a well-defined knowledge-based volitional action. We describe various characteristics of tasks as well as compare and contrast them with events. We propose two techniques for task extraction – i) using linguistic patterns and ii) using a BERT-based weakly supervised neural model. We evaluate our techniques with other competent baselines on 4 datasets from different domains. Overall, the BERT-based weakly supervised neural model generalizes better across multiple domains as compared to the purely linguistic patterns based approach.