Organizational and textual structuring of radio news discourse in English and Arabic.

The present work studies the overall structuring of radio news discourse via investigating three metatextual/interactive functions: (1) Discourse Organizing Elements (DOEs), (2) Attribution and (3) Sentential and Nominal Background Information (SBI & NBI). An extended corpus of about 73,000 words from BBC and Radio Damascus news is used to study DOEs and a restricted corpus of 38,000 words for Attribution and S & NBI. A situational approach is adopted to assess the influence of factors such as medium and audience on these functions and their frequence. It is found that: (1) DOEs are organizational and their frequency is determined by length of text; (2) Attribution Function in accordance with the editor's strategy and its frequency is audience sensitive; and (3) BI provides background information and is determined by audience and news topics. Secondly, the salient grammatical elements in DOEs are discourse deictic demonstratives, address pronouns and nouns referring to `the news'. Attribution is realized in reporting/reported clauses, and BI in a sentence, a clause or a nominal group. Thirdly, DOEs establish a hierarchy of (1) news, (2) summary/expansion and (3) item: including topic introduction and details. While Attribution is generally, and SBI solely, a function of detailing, NBI and proper names are generally a function of summary and topic introduction. Being primarily addressed to audience and referring metatextually, the functions investigated support Sinclair's interactive and autonomous planes of discourse. They also shed light on the part(s) of the linguistic system which realize the metatextual/interactive function. Strictly, `discourse structure' inevitably involves a rank-scale; but news discourse also shows a convention of item `listing'. Hence only within the boundary of variety (ultimately interpreted across language and in its situation) can textual functions and discourse structure be studied. Finally, interlingual variety study provides invaluable insights into a level of translation that goes beyond matching grammatical systems or situational factors, an interpretive level which has to be described in linguistic analysis of translation data.

Having a word with yourself:neural correlates of self-criticism and self-reassurance

Self-criticism is strongly correlated with a range of psychopathologies, such as depression, eating disorders and anxiety. In contrast, self-reassurance is inversely associated with such psychopathologies. Despite the importance of self-judgements and evaluations, little is known about the neurophysiology of these internal processes. The current study therefore used a novel fMRI task to investigate the neuronal correlates of self-criticism and self-reassurance. Participants were presented statements describing two types of scenario, with the instruction to either imagine being self-critical or self-reassuring in that situation. One scenario type focused on a personal setback, mistake or failure, which would elicit negative emotions, whilst the second was of a matched neutral event. Self-criticism was associated with activity in lateral prefrontal cortex (PFC) regions and dorsal anterior cingulate (dAC), therefore linking self-critical thinking to error processing and resolution, and also behavioural inhibition. Self-reassurance was associated with left temporal pole and insula activation, suggesting that efforts to be self-reassuring engage similar regions to expressing compassion and empathy towards others. Additionally, we found a dorsal/ventral PFC divide between an individual's tendency to be self-critical or self-reassuring. Using multiple regression analyses, dorsolateral PFC activity was positively correlated with high levels of self-criticism (assessed via self-report measure), suggesting greater error processing and behavioural inhibition in such individuals. Ventrolateral PFC activity was positively correlated with high self-reassurance. Our findings may have implications for the neural basis of a range of mood disorders that are characterised by a preoccupation with personal mistakes and failures, and a self-critical response to such events.

Cues to deception in a textual narrative context:lying in written witness statements

Little research has been undertaken into high stakes deception, and even less into high stakes deception in written text. This study addresses that gap. In this thesis, I present a new approach to detecting deception in written narratives based on the definition of deception as a progression and focusing on identifying deceptive linguistic strategy rather than individual cues. I propose a new approach for subdividing whole narratives into their constituent episodes, each of which is linguistically profiled and their progression mapped to identify authors’ deceptive strategies based on cue interaction. I conduct a double blind study using qualitative and quantitative analysis in which linguistic strategy (cue interaction and progression) and overall cue presence are used to predict deception in witness statements. This results in linguistic strategy analysis correctly predicting 85% of deceptive statements (92% overall) compared to 54% (64% overall) with cues identified on a whole statement basis. These results suggest that deception cues are not static, and that the value of individual cues as deception predictors is linked to their interaction with other cues. Results also indicate that in certain cue combinations, individual self-references (I, Me and My), previously believed to be indicators of truthfulness, are effective predictors of deceptive linguistic strategy at work

The readability of textual materials in biology

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Towards flexible parsing of structured textual model representations

Existing parsers for textual model representation formats such as XMI and HUTN are unforgiving and fail upon even the smallest inconsistency between the structure and naming of metamodel elements and the contents of serialised models. In this paper, we demonstrate how a fuzzy parsing approach can transparently and automatically resolve a number of these inconsistencies, and how it can eventually turn XML into a human-readable and editable textual model representation format for particular classes of models.