  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, we have concentrated our 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. We show that by combining the global information
of such a cluster with local decisions of a general classifier, a bootstrapping
cross-document classifier can be built to extract temporal relations between
events. Our experiments show that without any additional annotated data, the
accuracy of the proposed algorithm is higher than that of several previous
successful systems. The second proposed method for temporal relation extraction
is based on the expectation maximization (EM) algorithm. Within EM, we used
different techniques such as a greedy best-first search and integer linear
programming for temporal inconsistency removal. We think that the experimental
results of our EM based algorithm, as a first step toward a fully unsupervised
temporal relation extraction method, is encouraging.
