Bidirectional Integration of Pipeline Models

Traditional information extraction systems adopt pipeline strategies, which are highly ineffective and suffer from several problems such as error propagation. Typically, pipeline models fail to produce highly-accurate final output. On the other hand, there has been growing interest in integrated or joint models which explore mutual benefits and perform multiple subtasks simultaneously to avoid problems caused by pipeline models. However, building such systems usually increases computational complexity and requires considerable engineering. This paper presents a general, strongly-coupled, and bidirectional architecture based on discriminatively trained factor graphs for information extraction. First we introduce joint factors connecting variables of relevant subtasks to capture dependencies and interactions between them. We then propose a strong bidirectional MCMC sampling inference algorithm which allows information to flow in both directions to find the approximate MAP solution for all subtasks. Extensive experiments on entity identification and relation extraction using real-world data illustrate the promise of our approach.