@inproceedings{pawar-etal-2021-weakly,
title = "Weakly Supervised Extraction of Tasks from Text",
author = "Pawar, Sachin and
Palshikar, Girish and
Sinha Banerjee, Anindita",
editor = "Bandyopadhyay, Sivaji and
Devi, Sobha Lalitha and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 18th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2021",
address = "National Institute of Technology Silchar, Silchar, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2021.icon-main.71",
pages = "583--592",
abstract = "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.",
}
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%0 Conference Proceedings
%T Weakly Supervised Extraction of Tasks from Text
%A Pawar, Sachin
%A Palshikar, Girish
%A Sinha Banerjee, Anindita
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F pawar-etal-2021-weakly
%X 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.
%U https://aclanthology.org/2021.icon-main.71
%P 583-592
Markdown (Informal)
[Weakly Supervised Extraction of Tasks from Text](https://aclanthology.org/2021.icon-main.71) (Pawar et al., ICON 2021)
ACL
- Sachin Pawar, Girish Palshikar, and Anindita Sinha Banerjee. 2021. Weakly Supervised Extraction of Tasks from Text. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 583–592, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).