We present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an initial seed lexicon through a joint graph representation of words and documents. We report improved classification accuracies across multiple domains for the base models and the maximum entropy model bootstrapped from the PageRank annotation.

Schulte im Walde et al. (2008) presented a novel approach to semantic verb classication. The predicate argument model (PAC) presented in their paper models selectional preferences by using soft clustering that incorporates the Expectation Maximization (EM) algorithm and the MDL principle. In this paper, I will show how the model handles the task of differentiating between plausible and implau- sible combinations of verbs, subcategorization frames and arguments by applying the pseudo-disambiguation evaluation method. The predicate argument clustering model will be evaluated in comparison with the latent semantic clustering model by Rooth et al. (1999). In particular, the influences of the model parameters, data frequency, and the individual components of the predicate argument model are examined. The results of these experiments show that (i) the selectional preference model overgeneralizes over arguments for the purpose of a pseudo-disambiguation task and that (ii) pseudo-disambiguation should not be used as a universal indicator for the quality of a model.