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Verb Polarity Frames: a New Resource and its Application in Target-specific Polarity Classification
(2014)
We discuss target-specific polarity classification for German news texts. Novel, verb-specific features are used in a Simple Logistic Regression model. The polar perspective a verb casts on its grammatical roles is exploited. Also, an additional, largely neglected polarity class is examined: controversial texts. We found that the straightforward definition of ’controversial’ is problematic. More or less balanced polarities in a text are a poor indicator of controversy. Instead, non-polar wording helps more than polarity aggregation. However, our novel features proved useful for the remaining polarity classes.
This paper presents a hybrid pronoun resolution system for German. It uses a simple rule-driven entity-mention formalism to incrementally process discourse entities. Antecedent selection is performed based on Markov Logic Networks (MLNs). The hybrid architecture yields a cheap problem formulation in the MLNs w.r.t. inference complexity but pertains their expressiveness. We compare the system to a rule-driven baseline and an extension which uses a memory-based learner. We find that the MLN hybrid outperforms its competitors by large margins.
