As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
The ability to accurately classify temporal relation between events is an important task for a large number of natural language processing applications such as Question Answering (QA), Summarization, and Information Extraction. This paper presents a weakly-supervised machine learning approach for classification of temporal relation between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, it applies the hypothesis of “one type of temporal relation per discourse” and expands the scope of “discourse” from a single document to a cluster of topically-related documents. By combining the global information of such a cluster with local decisions of a general classifier, we propose a novel bootstrapping cross-document classifier to extract temporal relations between events. Our experiments show that without any additional annotated data, the accuracy of the proposed algorithm is at least 7% higher than that of the pattern based state of the art system.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.