A combined approach to emotion detection in suicide notes.

In this paper, we present the system we have developed for participating in the second task of the i2b2/VA 2011 challenge dedicated to emotion detection in clinical records. On the official evaluation, we ranked 6th out of 26 participants. Our best configuration, based upon a combination of both a machine-learning based approach and manually-defined transducers, obtained a 0.5383 global F-measure, while the distribution of the other 26 participants' results is characterized by mean = 0.4875, stdev = 0.0742, min = 0.2967, max = 0.6139, and median = 0.5027. Combination of machine learning and transducer is achieved by computing the union of results from both approaches, each using a hierarchy of sentiment specific classifiers.