Semantic Annotation of Japanese Functional Expressions and its Impact on Factuality Analysis
Yudai Kamioka, Kazuya Narita, Junta Mizuno, Miwa Kanno, Kentaro Inui · 2015
Recognizing the meaning of functional expressions is essential for natural language understanding.This is a difficult task, owing to the lack of a sufficient corpus for machine learning and evaluation.In this study, we design a new annotation scheme and construct a corpus containing 2,327 Japanese sentences and 8,775 functional expressions.Our scheme achieves high inter-annotator agreement with kappa score of 0.85.In the experiments, we confirmed that machine learning-based functional expression analysis contributes to factuality analysis.