NAIST.Japan: Temporal Relation Identification Using Dependency Parsed Tree

In this paper, we attempt to use a sequence labeling model with features from dependency parsed tree for temporal relation identification. In the sequence labeling model, the relations of contextual pairs can be used as features for relation identification of the current pair. Head-modifier relations between pairs of words within one sentence can be also used as the features. In our preliminary experiments, these features are effective for the temporal relation identification tasks. 1 Overview of our system This paper presents a temporal relation identifier by the team NAIST.Japan. Our identifier has two charactaristics: sequence labeling model and use of dependency parsed tree. Firstly, we treated each problem a sequence labeling problem, such that event/time pairs were ordered by the position of the events and times in the document. This idea is for task B and C. In task B, the neighbouring relations between an EVENT and DCT-TIMEX3 tend to interact. In task C, when EVENT-a, EVENT-b, and EVENT-c are linearly ordered, the relation between EVENT-a and EVENTb tends to affect the one between EVENT-b and EVENT-c. Secondly, we introduced dependency features where each word was annotated with a label indicating its tree position to the event and the time, e.g. “descendant” of the event and “ancestor” of the time. The dependency features are introduced for our machine learning-based relation identifier. In task A, we need to label several different event-time pairs within the same sentence. We can use information from TIMEX3, which is a descendent of the target EVENT in the dependency tree. Section 2 shows how to use a sequence labeling model for the task. Section 3 shows how to use the dependency parsed tree for the model. Section 4 presents the results and discussions. 2 Temporal Relation Identification by Sequence Labeling Our approach to identify temporal relation is based on a sequence labeling model. The target pairs are linearly ordered in the texts. Sequence labeling model can be defined as a method to estimate an optimal label sequence over an observed sequence . We consider, -parameterized function Here, denotes all possible label combinations over ; denotes a feature expression over . Introducing a kernel function: