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In this paper, we explore different linguistic structures encoded as convolution kernels for the detection of subjective expressions. The advantage of convolution kernels is that complex structures can be directly provided to a classifier without deriving explicit features. The feature design for the detection of subjective expressions is fairly difficult and there currently exists no commonly accepted feature set. We consider various structures, such as constituency parse structures, dependency parse structures, and predicate-argument structures. In order to generalize from lexical information, we additionally augment these structures with clustering information and the task-specific knowledge of subjective words. The convolution kernels will be compared with a standard vector kernel.
We examine moments in social interaction in which a person formulates what another thinks or believes. Such formulations of belief constitute a practice with specifiable contexts and consequences. Belief formulations treat aspects of the other person's prior conduct as accountable on the basis that it provided a new angle on a topic, or otherwise made a surprising contribution within an ongoing course of actions. The practice of belief formulations subjectivizes the content that the other articulated and thereby topicalizes it, mobilizing commitment to that position, an account, or further elaboration. We describe how the practice can be put to work in different activity contexts: sometimes it is designed to undermine the other's position as a subjective 'mere belief', at other times it serves to mobilize further topic talk. Throughout, belief formulations show themselves to be a method by which we get to know ourselves and each other as mental agents.