Abstract

Background. Traditional systems for relation extraction [1, 7] partition the process into several subtasks and solve them incrementally (ie, detecting entities from text, labeling their types and then extracting their relations). Such systems treat the subtasks independently and so may propagate errors across subtasks in the process. Recent studies [18, 29] focus on joint extraction methods to capture the inherent linguistic dependencies between relations and entity arguments (eg, the types of entity arguments help determine their relation type, and vice versa) to resolve error propagation. A major challenge in joint extraction of typed entities and relations is to design domain-independent systems that will apply to text corpora from different domains in the absence of human-annotated, domain data. The process of manually labeling a training set with a large number of entity and relation types is too expensive and error …

Date

2017

Authors

Xiang Ren

Book

Proceedings of the 2017 ACM International Conference on Management of Data

Pages

52-54