In this research we aim to detect subjective sentences in spontaneous speech and label them for polarity. We introduce a novel technique wherein subjective patterns are learned from both labeled and unlabeled data, using n-grams with varying levels of lexical instantiation. Applying this technique to meeting speech, we gain significant improvement over state-of-the-art approaches and demonstrate the methodís robustness to ASR errors. We also show that coupling the pattern-based approach with structural and lexical features of meetings yields additional improvement.
Bibliographic reference. Murray, Gabriel / Carenini, Giuseppe (2009): "Detecting subjectivity in multiparty speech", In INTERSPEECH-2009, 2007-2010.