Detecting Contradictions Within and Across Documents

Abstract

This paper presents DocChecker, a system for detecting textual contradictions. Given a document collection 𝒟, DocChecker identifies conflicting properties or relationships assigned to the same entity, both within and across documents. It adopts a graph-based paradigm: entities and their relations are extracted, represented as a property graph G , joined across documents, and enriched with domain knowledge. DocChecker introduces a class of rules that detect vertex- and edge-level contradictions in PTIME, unifying logical reasoning, ML predictions and NLP tools. We present how graphs are extracted, joined and enriched, and propose a parallel rule discovery algorithm with new generation, evaluation and validation strategies tailored for sparse contradictions. Using real-life documents, we empirically verify that DocChecker detects intra- and inter-document contradictions with an average F1-score of 0.900, up to 0.935, and it scales to large collections 𝒟 in parallel.