Using Tree Kernels for Classifying Temporal Relations between Events
Seyed Abolghasem Mirroshandel, Gholamreza Ghassem-Sani, Mahdy Khayyamian · Institutional Repositories DataBase (IRDB) · 2009
The ability to accurately classify temporal relations between events is an important task in a large number of natural language processing and text mining applications such as question answering, summarization, and language specific information retrieval.In this paper, we propose an improved way of classifying temporal relations, using support vector machines (SVM).Along with gold-standard corpus features, the proposed method aims at exploiting useful syntactic features, which are automatically generated, to improve accuracy of the SVM classification method.Accordingly, a number of novel kernel functions are introduced and evaluated for temporal relation classification.Our evaluations clearly demonstrate that adding syntactic features results in a considerable performance improvement over the state of the art method, which merely employs gold-standard features.