Towards Unsupervised Learning of Temporal Relations Between Events
Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as question answering, information extraction and summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, the authors have concentrated their efforts to reduce the need for annotated data as much as possible. This paper presents two different algorithms towards this goal. The first algorithm is a weakly supervised machine learning approach for classification of temporal relations between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, inspired by the hypothesis of "One type of temporal relation per discourse", it extracts useful information from a cluster of topically related documents.