Structured information resulting from temporal information processing is
crucial for a variety of natural language processing tasks, for instance to
generate timeline summarization of events from news documents, or to answer
temporal/causal-related questions about some events. In this thesis we present
a framework for an integrated temporal and causal relation extraction system.
We first develop a robust extraction component for each type of relations, i.e.
temporal order and causality. We then combine the two extraction components
into an integrated relation extraction system, CATENA---CAusal and Temporal
relation Extraction from NAtural language texts---, by utilizing the
presumption about event precedence in causality, that causing events must
happened BEFORE resulting events. Several resources and techniques to improve
our relation extraction systems are also discussed, including word embeddings
and training data expansion. Finally, we report our adaptation efforts of
temporal information processing for languages other than English, namely
Italian and Indonesian.

本文提出一个集成时间和因果关系提取的框架，在该框架中，通过利用因果关系中事件先行的假设，将两个提取组件整合成一个系统，使用词嵌入和训练数据扩展等方法来改善关系提取系统，并介绍了将时间信息处理应用于其他语言的适应性尝试。