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Negation is an important contextual phenomenon that needs to be addressed in sentiment analysis. Next to common negation function words, such as not or none, there is also a considerably large class of negation content words, also referred to as shifters, such as the verbs diminish, reduce or reverse. However, many of these shifters are ambiguous. For instance, spoil as in spoil your chance reverses the polarity of the positive polar expression chance while in spoil your loved ones, no negation takes place. We present a supervised learning approach to disambiguating verbal shifters. Our approach takes into consideration various features, particularly generalization features.
Research on syntactic ambiguity resolution in language comprehension has shown that subjects' processing decisions are influenced by a variety of heterogeneous factors such as e.g., syntactic complexity, semantic fit and the discourse frequency of the competing structures. The present paper investigates a further potentially relevant factor in such processes: effects of syntagmatic lexical chunking (or matching to a complex memorized prefab) whose occurrence would be predicted from usage-based assumptions about linguistic categorisation. Focusing on the widely studied so-called DO/SC-ambiguity in which a post-verbal NP is syntactically ambiguous between a direct object and the subject of an embedded clause, potentially biasing collocational chunks of the relevant type are identified in a number of corpus-linguistic pretests and then investigated in a self-paced reading experiment. The results show a significant increase in processing difficulty from a collocationally neutral over a lexically biasing to a strongly biasing condition. This suggests that syntagmatically complex and partially schematic templates of the kind envisioned in usage-based Construction Grammar may impinge on speakers' online processing decisions during sentence comprehension.