Applying machine learning to Chinese temporal relation resolution

Abstract

Temporal relation resolution involves extraction of temporal information explicitly or implicitly embedded in a language. This information is often inferred from a variety of interactive grammatical and lexical cues, especially in Chinese. For this purpose, inter-clause relations (temporal or otherwise) in a multiple-clause sentence play an important role. In this paper, a computational model based on machine learning and heterogeneous collaborative bootstrapping is proposed for analyzing temporal relations in a Chinese multiple-clause sentence. The model makes use of the fact that events are represented in different temporal structures. It takes into account the effects of linguistic features such as tense/aspect, temporal connectives, and discourse structures. A set of experiments has been conducted to investigate how linguistic features could affect temporal relation resolution.