Miscommunication in the institutional context of the broadcast news interview : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Psychology at Massey University, Palmerston North, New Zealand

This study examined the pattern and relative success of linguistic interaction in the Broadcast News Interview (BNI). BNI is modelled as a genre of institutional communication. The psychological and functional characteristics of the BNI were examined from the viewpoint of how communicative conventions that normally regulate interview performance may, at times, impede effective communication. The BNI is intended to transfer information from an expert witness to an interested, though relatively uninformed audience. The interviewer is supposed to act as both conduit and catalyst. Pragmatic properties of the interlocutors' speech as they orient themselves towards the context of the conversation was analysed in order to reveal the manner in which prior assumptions or beliefs may lead to faulty inferences. The notion of miscommunication is used to describe and explain the faults associated with processes of representing the illocutionary force of an utterance, rather than deficiencies in pronunciation or auditory sensation and perception. Opting for a qualitative analysis, an attempt was made to ground explanations in relevant theoretical models of interpersonal communication and communication failure. Results indicate that the conventions that distinguish the BNI from more mundane types of interaction impede successful communication. The study highlights that participants who wish to attain their communicative goal must be more aware of the functional procedures of the BNI and anticipate impediments to successful communication

Simple stopping criteria for information theoretic feature selection

Feature selection aims to select the smallest feature subset that yields the minimum generalization error. In the rich literature in feature selection, information theory-based approaches seek a subset of features such that the mutual information between the selected features and the class labels is maximized. Despite the simplicity of this objective, there still remain several open problems in optimization. These include, for example, the automatic determination of the optimal subset size (i.e., the number of features) or a stopping criterion if the greedy searching strategy is adopted. In this paper, we suggest two stopping criteria by just monitoring the conditional mutual information (CMI) among groups of variables. Using the recently developed multivariate matrix-based Renyi's \alpha-entropy functional, which can be directly estimated from data samples, we showed that the CMI among groups of variables can be easily computed without any decomposition or approximation, hence making our criteria easy to implement and seamlessly integrated into any existing information theoretic feature selection methods with a greedy search strategy.Comment: Paper published in the journal of Entrop