期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2012
卷号：2012
出版社：ACL Anthology
摘要：Most previous studies in computerized deception
detection have relied only on shallow
lexico-syntactic patterns. This paper
investigates syntactic stylometry for
deception detection, adding a somewhat
unconventional angle to prior literature.
Over four different datasets spanning from
the product review to the essay domain,
we demonstrate that features driven from
Context Free Grammar (CFG) parse trees
consistently improve the detection performance
over several baselines that are based
only on shallow lexico-syntactic features.
Our results improve the best published result
on the hotel review data (Ott et al.,
2011) reaching 91.2% accuracy with 14%
error reduction.
