Constructing a Temporal Relation Tagged Corpus of Chinese based on Dependency Structure

Abstract

This paper describes an annotation guideline for a temporal relation tagged corpus. Our goal is to construct a machine learnable model which automatically analyzes temporal events and relations between events. In this paper, we report our initial attempt in preparing a small-sized tagged corpus used as a training data. Since analyzing all combinations of events is inefficient, we examine use of dependency structure to efficiently recognize meaningful temporal relations. We find that the dependency structure appears useful for reducing manual efforts in constructing tagged corpus with temporal relations.