Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2012 | ACL | computerized deception detection,context free grammar,shallow lexico-syntactic feature,error reduction,product review,different datasets,hotel review data,shallow lexico-syntactic pattern,deception detection,syntactic stylometry,detection performance |
Field | DocType | Volume |
Context-free grammar,Deception,Computer science,Speech recognition,Stylometry,Artificial intelligence,Natural language processing,Product reviews,Parsing,Syntax,Machine learning | Conference | P12-2 |
Citations | PageRank | References |
92 | 4.19 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Feng | 1 | 280 | 19.55 |
| Ritwik Banerjee | 2 | 119 | 6.14 |
| Yejin Choi | 3 | 2239 | 153.18 |