A Study on Detecting Fact vs Non-Fact in News Articles.

Date of Submission

December 2016

Date of Award

Winter 12-12-2017

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Computer Vision and Pattern Recognition Unit (CVPR-Kolkata)


Majumdar, Debapriyo (CVPR-Kolkata; ISI)

Abstract (Summary of the Work)

News articles are a major source of facts about the current state and events of our surrounding world. In this thesis, we consider the problem of detecting factual and non-factual parts from news articles. We present a comprehensive survey on the existing literature on fact classification on news articles as well as a related and more widely studied problem of subjectivity vs objectivity classification of statements. We present experiments on classifying facts and non-facts from news articles using several features and combinations of those on two datasets, one of which was used for subjectivity classification in previous works. We show that standard textual dataset dependent features such as n-grams produce good results on both datasets, but more general features such as part of speech tags and entity types produce inconsistent results. We analyze the results based on the nature of the datasets to present insights on the usefulness of the features and their applicability in the classification task we are considering.


ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28843245

Control Number


Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.



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