Abstract
The more we are advancing towards a modern world, the more it opens the path to falsification in every aspect of life. Even in case of knowing the surrounding, common people can not judge the actual scenario as the promises, comments and opinions of the influential people at power keep changing every day. Therefore computationally determining the truthfulness of such claims and comments has a very important societal impact. This paper describes a unique method to extract check-worthy claims from the 2016 US presidential debates and verify the truthfulness of the check-worthy claims. We classify the claims for check-worthiness with our modified Tf-Idf model which is used in background training on fact-checking news articles (NBC News and Washington Post). We check the truthfulness of the claims by using POS, sentiment score and cosine similarity features.- Anthology ID:
- 2020.icon-main.20
- Volume:
- Proceedings of the 17th International Conference on Natural Language Processing (ICON)
- Month:
- December
- Year:
- 2020
- Address:
- Indian Institute of Technology Patna, Patna, India
- Editors:
- Pushpak Bhattacharyya, Dipti Misra Sharma, Rajeev Sangal
- Venue:
- ICON
- SIG:
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 155–160
- Language:
- URL:
- https://aclanthology.org/2020.icon-main.20
- DOI:
- Cite (ACL):
- Shukrity Si, Anisha Datta, and Sudip Naskar. 2020. A New Approach to Claim Check-Worthiness Prediction and Claim Verification. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 155–160, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).
- Cite (Informal):
- A New Approach to Claim Check-Worthiness Prediction and Claim Verification (Si et al., ICON 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.icon-main.20.pdf