Hedge Detection and Scope Finding by Sequence Labeling with Normalized Feature Selection∗

This paper presents a system which adopts a standard sequence labeling technique for hedge detection and scope finding. For hedge detection, we formulate it as a hedge labeling problem, while for hedge scope finding, we use a two-step labeling strategy, one for hedge labeling and the other for scope finding. In particular, various kinds of syntactic dependencies 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.