TY  - JOUR
PY  - 2012//
TI  - Discovering fine-grained sentiment in suicide notes
JO  - Biomedical informatics insights
A1  - Wang, Wenbo
A1  - Chen, Lu
A1  - Tan, Ming
A1  - Wang, Shaojun
A1  - Sheth, Amit P.
SP  - 137
EP  - 145
VL  - 5
IS  - Suppl 1
N2  - This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system gains a better trade-off between precision and recall, and yields the highest micro-averaged F-measure (0.5038), which is better than the mean (0.4875) and median (0.5027) micro-average F-measures among all participating teams.<p />  <p>Language: en</p>
LA  - en
SN  - 1178-2226
UR  - http://dx.doi.org/10.4137/BII.S8963
ID  - ref1
ER  -