Hedge Detection and Scope Finding by Sequence Labeling with Procedural Feature Selection
Shaodian Zhang, Hai Zhao, Guodong Zhou, Bao‐Liang Lu · Conference on Computational Natural Language Learning · 2010
This paper presents a system which adopts a standard sequence labeling technique for hedge detection and scope finding. For the first task, hedge detection, we formulate it as a hedge labeling problem, while for the second task, we use a two-step labeling strategy, one for hedge cue labeling and the other for scope finding. In particular, various kinds of syntactic features are systemically exploited and effectively integrated using a large-scale normalized feature selection method. Evaluation on the CoNLL-2010 shared task shows that our system achieves stable and competitive results for all the closed tasks. Furthermore, post-deadline experiments show that the performance can be much further improved using a sufficient feature selection.