Recent work on evaluativity or sentiment in the language sciences has focused on the contributions that lexical items provide. In this paper, we discuss contextual evaluativity, stance that is inferred from lexical meaning and pragmatic environments. Focusing on assessor-grounding claims like We liked him because he so clearly disliked Margaret Thatcher, we build a corpus and construct a system employing compositional principles of evaluativity calculation to derive that we dislikes Margaret Thatcher. The resulting system has an F-score of 0.90 on our dataset, outperforming reasonable baselines, and indicating the viability of inferencing in the evaluative domain. A central aim of contemporary research on sentiment or evaluative language is the extraction of evaluative triples: evaluator, target, evaluation . To date, both formal (e.g., Martin and White 2005, Potts 2005) and computational approaches (e.g., Pang and Lee 2008) have focused on how such triples are lexically encoded (e.g., the negative affect of scoundrel or dislike). While lexical properties are a key source of evaluative information, word-based considerations alone can miss pragmatic inferences resulting from context. (1), for example, communicates that the referent of we bears not only positive stance towards the referent of him, but also negative stance towards Margaret Thatcher: (1) We liked him because he so clearly disliked Margaret Thatcher. LEXICAL EVALUATIVITY: we, him, + ; he, M.T., -CONTEXTUAL EVALUATIVITY: we, M.T., -This paper argues for a compositional approach to contextual evaluativity similar to the compositional methods adopted for lexical evaluativity in Moilanen and Pulman (2007) and Nasukawa and Yi (2003) . At the the heart of the approach is the treatment of verbal predicates (dislike in (1)) as evaluativity functors which relate argument/entity-level evaluativity to event-level evaluativity. As discussed in §2, the utitlity of such a model surfaces in cases where the event-level evaluativity is known from context, and thus new information about the contextual evaluativity of the event participants (e.g. Margaret Thatcher) can be inferred. Consequently, the empirical focus of this paper is on structures like (1), where the second clause provides grounds for the sentiment encoded in the first, and hence has a predictable event-level evaluation from the first clause's evaluator. In §3 we describe the collection and annotation of a corpus of such assessment-grounding configurations from large-scale web data. This annotated corpus serves as a test bed for experimental evaluation of various implementations of the proposed compositional approach. The results of these experiments ( §4) strongly support a compositional approach to contextual evaluativity inference. A simple compositional algorithm based on a small, manually created evaluativity functor lexicon demonstrated significantly better precision than non-compositional baselines. Moreover, a method for automatically expanding coverage to novel predicates based on similarity with the manually created lexicon is shown to increase recall dramatically with modest reduction in precision.
REF propose the concept of predicate-specific evaluativity functors which allow for inferring the writers' evaluation of a proposition based on the evaluation of the arguments of the predicate.
Extracting Contextual Evaluativity
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)
Psychology Computer Science