Temporal Relation Extraction Using Expectation Maximization

The ability to accurately determine temporal relations between events is an important task for several natural language processing applications such as Question Answering, Summarization, and Information Extraction. Since current supervised methods require large corpora, which for many languages do not exist, we have focused our attention on approaches with less supervision as much as possible. This paper presents a fully generative model for temporal relation extraction based on the expectation maximization (EM) algorithm. Our experiments show that the performance of the proposed algorithm, regarding its little supervision, is considerable in temporal relation learning.