2019 年 26 巻 2 号 p. 329-359
To communicate with humans in a human-like manner, systems need to understand behavior and psychological states in situations of human-machine interactions, such as in the cases of autonomous driving and nursing robots. We focus on driving situations as they are part of our daily lives and concern safety. To develop such systems, a corpus annotated with behavior and subjectivity in driving situations is necessary. In this study, subjectivity includes emotions, polarity, sentiments, human judgments, perceptions, and cognitions. We construct a driving experience corpus (DEC) (261 blog articles, 8,080 sentences) with four manually annotated tags. First, we annotate spans with driving experience tags (DE). Then, three tags, other’s behavior (OB), self-behavior (SB), and subjectivity (SJ), are annotated within DE spans. In addition to describing the guidelines, we present corpus specifications, agreement between annotators, and three major difficulties during the development: the extended self, important information, and voice in mind. Automatic annotation experiments were conducted on the DEC using Conditional Random Fields-based methods. On the test set, the F-scores were about .55 for both OB and SB and approximately. 75 for SJ, respectively. We provide error analysis that reveals difficulties in interpreting nominatives and differentiating behavior from subjectivity.
