This paper reports experiments for the
CoNLL-2010 shared task on learning to
detect hedges and their scope in natural language text.
We have addressed
the experimental tasks as supervised linear maximum margin prediction problems. For sentence level hedge detection
in the biological domain we use an L1-regularisedbinarysupportvectormachine,
while for sentence level weasel detection
in the Wikipedia domain, we use an L2-regularised approach. We model the in-
sentence uncertainty cue and scope detection task as an L2-regularised approxi-
mate maximum margin sequence labelling
problem, using the BIO-encoding. In addition to surface level features, we use a
variety of linguistic features based on a
functional dependency analysis. A greedy
forward selection strategy is used in exploring the large set of potential features.
Our official results for Task 1 for the biological domain are 85.2 F1-score, for the
Wikipedia set 55.4 F1-score. For Task 2,
our official results are 2.1 for the entire
task with a score of 62.5 for cue detection. After resolving errors and final bugs,
our final results are for Task 1, biological: 86.0, Wikipedia: 58.2; Task 2, scopes:
39.6 and cues: 78.5.