Truth Discovery of Multi-Source Text Data

Chen CHANG  Jianjun CAO  Qin FENG  Nianfeng WENG  Yuling SHANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.11   pp.2249-2252
Publication Date: 2019/11/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8267
Type of Manuscript: LETTER
Category: Fundamentals of Information Systems
truth discovery,  ant colony optimization,  text mining,  

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Most existing truth discovery approaches are designed for structured data, and cannot meet the strong need to extract trustworthy information from raw text data for its unique characteristics such as multifactorial property of text answers (i.e., an answer may contain multiple key factors) and the diversity of word usages (i.e., different words may have the same semantic meaning). As for text answers, there are no absolute correctness or errors, most answers may be partially correct, which is quite different from the situation of traditional truth discovery. To solve these challenges, we propose an optimization-based text truth discovery model which jointly groups keywords extracted from the answers of the specific question into a set of multiple factors. Then, we select the subset of multiple factors as identified truth set for each question by parallel ant colony synchronization optimization algorithm. After that, the answers to each question can be ranked based on the similarities between factors answer provided and identified truth factors. The experiment results on real dataset show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art approaches.