Checking Fact Worthiness using Sentence Embeddings
This work provides an incremental improvement for automating fact-checking, which is a time-consuming task for journalists and fact-checkers.
This paper addresses the problem of automatically identifying fact-worthy statements in text, a task previously performed manually. The authors experimented with Sentence-BERT embeddings, topic modeling, and sentiment scores, demonstrating improvements in results as measured by MAP, Mean Reciprocal Rank, Mean R-Precision, and Mean Precision@N.
Checking and confirming factual information in texts and speeches is vital to determine the veracity and correctness of the factual statements. This work was previously done by journalists and other manual means but it is a time-consuming task. With the advancements in Information Retrieval and NLP, research in the area of Fact-checking is getting attention for automating it. CLEF-2018 and 2019 organised tasks related to Fact-checking and invited participants. This project focuses on CLEF-2019 Task-1 Check-Worthiness and experiments using the latest Sentence-BERT pre-trained embeddings, topic Modeling and sentiment score are performed. Evaluation metrics such as MAP, Mean Reciprocal Rank, Mean R-Precision and Mean Precision@N present the improvement in the results using the techniques.