Event Graph Based Contradiction Recognition from Big Data Collection
Conventional models relying on similarity utilizing low-level surface statistical, syntactic and lexical semantic features are suboptimal in contradiction recognition, especially for the large-scale data, such as the senor and traffic data. To tackle this problem, this work treats the text and hypothesis pair as event graph and proposes a novel model based on event graph to recognize contradiction in big data collection. The proposed model is capable of seamlessly sewing the high-level event semantic features corresponding to the conflicting linguistic phenomena to identify contradictory construction. Experimental results show that our event graph based contradiction recognition model achieves significant improvement as compared to state-of-the-art competitors.
event graph contradiction recognition big data high-level event semantic feature atomic event event predicate event semantic role