Entity Detection for Check-worthiness Prediction: Glasgow Terrier at CLEF CheckThat! 2019

Since information can be created and shared online by anyone, a lot of time and effort are required to manually fact-check all the information encountered by users everyday. Hence, an automatic factchecking process is needed to effectively fact-check the vast information available online. However, gathering information related to every single claim can also be redundant, as not all sentences or articles are checkworthy. In this paper, we propose an effective approach for retrieving check-worthy sentences within American political debates, which relates to the first task of the CLEF CheckThat! 2019 Lab. To rank sentences based on their check-worthiness, we propose to represent each sentence using their mentioned entities using a TF-IDF representation. We use a SVM classifier to predict the check-worthiness of each sentence. Our approach ranked 4th out of 12 submissions. Our experiments show that the pronouns and coreference resolution pre-processing procedure we use as part of our approach does improve the effectiveness of sentence checkworthiness prediction. Furthermore, our results show that entity analysis features provide valuable evidence for this task.