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Background Knowledge in Learning-Based Relation Extraction.

机译：基于学习的关系抽取的背景知识。

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In this thesis, we study the importance of background knowledge in relation extraction systems. We not only demonstrate the bene ts of leveraging background knowledge to improve the systems' performance but also propose a principled framework that allows one to effectively incorporate knowledge into statistical machine learning models for relation extraction. Our work is motivated by the fact that relation extraction systems in the literature usually use evidence that is written explicitly in the input text to detect and characterize the semantic relations between target concepts. Although this approach achieves reasonable performance, it does not necessarily guarantee accurate extraction due to problems of poor information representation of the systems' inputs and lack of knowledge to support logical reasoning. We argue that relation extraction systems would benefit from using one or more background knowledge sources, both in enriching the systems' inputs and biasing the final outputs. We illustrate our framework in the context of several learning-based relation extraction tasks. The first task is Taxonomic Relation identification where we employ an external knowledge source to construct meaning representation of the task inputs and support global inference to identify taxonomic relations between input terms. In the second task, Event Relation Discovery, we focus on identify causality relation between events in text. Our approach leverages background knowledge to perform joint inference among several classifiers that make local decisions on event causality relation. After that, we study the problem of constructing a timeliine of events extracted from text, Event Timeline Construction. To address this task, we propose a new timeline representation with events mapped to absolute time intervals. In this work, we present a time interval-based global inference model that jointly assigns events into time intervals on a timeline and orders events temporally.

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