Fact Discovery for Text Data

Fact extraction, which aims to extract (entity, attribute, value)-tuples from massive text corpora, is crucial in text data mining. Recent approaches focus on extracting facts by mining textual patterns with semantic types, where the quality of a pattern is evaluated based on content-based criteria, such as frequency. However, these approaches overlook the dimension of pattern reliability, which reflects how likely the extracted facts are correct. As a result, a pattern of good content quality (e.g., high frequency) may still extract incorrect facts. In this chapter, we consider both pattern reliability and fact trustworthiness in addressing the pattern-based fact extraction problem [1]. We give a motive example and the problem definition in Sects. 5.1 and 5.2, respectively. We detail the CNN-LSTM model design and present the experimental results in Sect. 5.3. Next, we conclude in Sect. 5.4.