Graph-Based Jointly Modeling Entity Detection and Linking in Domain-Specific Area

Abstract

The current state-of-the-art Entity Detection and Linking (EDL) systems are geared towards general corpora and cannot be directly applied to the specific domain effectively due to the fact that texts in domain-specific area are often noisy and contain phrases with ambiguous meanings that easily could be recognized as entity mention by traditional EDL methods but actually should not be linked to real entities (i.e., False Entity mention (FEM)). Moreover, in most current EDL literatures, ED (Entity Detection) and EL (Entity Linking) are frequently treated as equally important but separate problems and typically performed in a pipeline architecture without considering the mutual dependency between these two tasks. Therefore, to rigorously address the domain-specific EDL problem, we propose an iterative graph-based algorithm to jointly model the ED and EL tasks in domain-specific area by capturing the local dependency of mention-to-entity and the global interdependency of entity-to-entity. We extensively evaluated the performance of proposed algorithm over a data set of real world movie comments, and the experimental results show that the proposed approach significantly outperforms the baselines and achieve 82.7\(\%\) F1 score for ED and 89.0\(\%\) linking accuracy for EL respectively.