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Title: Precise temporal slot filling 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 work denoted 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. Based on our observation from a news corpus, most of the facts should have the two points, however, fewer than 0.1% of them have time expressions in the documents. On the other hand, the documents’ post time, though often available, is not as precise as the time expressions of being the time a fact was valid. Therefore, directly decomposing the time expressions or using an arbitrary post-time period cannot provide accurate results for PTSF. 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 an unsupervised approach based on the philosophy of truth finding. The approach has two modules that mutually enhance each other: One is a reliability estimator of fact extractors conditionally on the temporal contexts; the other is a fact trustworthiness estimator based on the extractor’s reliability. Commonsense knowledge (e.g., one country has only one president at a specific time) was automatically generated from data and used for inferring false claims based on trustworthy facts. For the purpose of evaluation, we manually collect hundreds of temporal facts from Wikipedia as ground truth, including country’s presidential terms and sport team’s player career history. Experiments on a large news dataset demonstrate the accuracy and efficiency of our proposed algorithm.  more » « less
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Knowledge and Information Systems
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National Science Foundation
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