Improving Information Extraction via Truth Finding with Data-Driven Commonsense

The task of temporal slot filling (TSF) is to extract values of specific attributes for a given entity, called ``facts'', as well as temporal tags of the facts, from text data. While existing works denote the temporal tags as single time slots, in this paper, we introduce and study the task of Precise TSF (PTSF), that is to fill two precise temporal slots including the beginning and ending time points. The challenge of PTSF lies in finding precise time tags in noisy and incomplete temporal contexts in the text. To address the challenge, we propose one unsupervised approach based on the philosophy of truth finding. Commonsense knowledge was automatically generated from data and used for inferring false claims based on trustworthy facts. Experiments on a large news dataset demonstrate the accuracy and efficiency of our proposed algorithm. Furthermore, we also explore the possibility on using probabilistic graphical modeling to solve the same problem.