An Automatic Approach to Harvesting Temporal Knowledge of Entity Relationships
Chuanyan Zhang, Hong Xiaoguang, Peng Zhaohui · Procedia Engineering · 2012
Traditional relation extraction systems seek to distill semantic relational facts from natural language text by assuming that facts are time-invariant. However, relations have associated validity intervals; time-dependent relations seem to be far more common than time-invariant ones. Therefore, relations should include time as a first-class dimension. In this paper, we present an approach for automatically harvesting temporal knowledge of entity relationships. Our extraction framework is bootstrapping, by taking the relation instance as a separate knowledge dimensions. The discriminate MNLs can soften hard rules which are usually applied in bootstrapping relation extraction systems, by learning their weights in a maximum likelihood estimate sense. In order to avoid the manually marked training data, we first generate the training data based on heuristic method, and patterns are selected by doing L1-norm regularized maximum likelihood estimation. The experiments show that our framework is domain-independent, and can automatically and effectively harvest temporal knowledge of relations.