Translating Popular Science: Professional Translators vs Field Experts

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Representing and Redefining Specialised Knowledge: Medical Discourse

This volume brings together five selected papers on medical discourse which show how specialised medical corpora provide a framework that helps those engaging with medical discourse to determine how the everyday and the specialised combine to shape the discourse of medical professionals and non-medical communities in relation to both long and short-term factors. The papers contribute, in an exemplary way, to illustrating the shifting boundaries in today’s society between the two major poles making up the medical discourse cline: healthcare discourse at the one end, which records the demand for personalised therapies and individual medical services; and clinical discourse the other, which documents research into society’s collective medical needs

Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery

The exponential growth of scientific literature is creating an increased need for systems to process and assimilate knowledge contained within text. Literature Based Discovery (LBD) is a well established field that seeks to synthesize new knowledge from existing literature, but it has remained primarily in the theoretical realm rather than in real-world application. This lack of real-world adoption is due in part to the difficulty of LBD, but also due to several solvable problems present in LBD today. Of these problems, the ones in most critical need of improvement are: (1) the over-generation of knowledge by LBD systems, (2) a lack of meaningful evaluation standards, and (3) the difficulty interpreting LBD output. We address each of these problems by: (1) developing indirect relatedness measures for ranking and filtering LBD hypotheses; (2) developing a representative evaluation dataset and applying meaningful evaluation methods to individual components of LBD; (3) developing an interactive visualization system that allows a user to explore LBD output in its entirety. In addressing these problems, we make several contributions, most importantly: (1) state of the art results for estimating direct semantic relatedness, (2) development of set association measures, (3) development of indirect association measures, (4) development of a standard LBD evaluation dataset, (5) division of LBD into discrete components with well defined evaluation methods, (6) development of automatic functional group discovery, and (7) integration of indirect relatedness measures and automatic functional group discovery into a comprehensive LBD visualization system. Our results inform future development of LBD systems, and contribute to creating more effective LBD systems

Using Noun Phrases for Navigating Biomedical Literature on Pubmed: How Many Updates Are We Losing Track of?

Author-supplied citations are a fraction of the related literature for a paper. The “related citations” on PubMed is typically dozens or hundreds of results long, and does not offer hints why these results are related. Using noun phrases derived from the sentences of the paper, we show it is possible to more transparently navigate to PubMed updates through search terms that can associate a paper with its citations. The algorithm to generate these search terms involved automatically extracting noun phrases from the paper using natural language processing tools, and ranking them by the number of occurrences in the paper compared to the number of occurrences on the web. We define search queries having at least one instance of overlap between the author-supplied citations of the paper and the top 20 search results as citation validated (CV). When the overlapping citations were written by same authors as the paper itself, we define it as CV-S and different authors is defined as CV-D. For a systematic sample of 883 papers on PubMed Central, at least one of the search terms for 86% of the papers is CV-D versus 65% for the top 20 PubMed “related citations.” We hypothesize these quantities computed for the 20 million papers on PubMed to differ within 5% of these percentages. Averaged across all 883 papers, 5 search terms are CV-D, and 10 search terms are CV-S, and 6 unique citations validate these searches. Potentially related literature uncovered by citation-validated searches (either CV-S or CV-D) are on the order of ten per paper – many more if the remaining searches that are not citation-validated are taken into account. The significance and relationship of each search result to the paper can only be vetted and explained by a researcher with knowledge of or interest in that paper

A Cognitive Linguistic Study of Categorisation and Uncertain Reasoning in the Representation of Degree Modifiers.

Degree modifiers (such as very and really) are common features of written and spoken language. In general, their effect is to moderate the perceived strength of the linguistic form on which they act, making them a useful and versatile tool of expression and emphasis. However, the cognitive mechanisms that underlie the conceptualisation of degree modifiers and the linguistic aspects of their use in combination with other classes of words are extremely complex. The ease and fluency with which they are used and the extent to which their effect is commonly understood is good evidence that, like many aspects of meaning, degree modifiers rely on commonly held beliefs and knowledge about the world around us. For this reason the whole area of linguistic categorisation and prototypes are central to understanding the role of degree modifiers, particularly given that assumptions about prototypical strengths of adjectives are exactly what degree modifiers seek to alter. A core part of this study is the consideration of the role of uncertainty - not only uncertainty relating to the strength of the degree modifier, but also of the linguistic forms on which they act. More specifically, the inter-relationship between the perceived strength of degree modifiers and the certainty (or uncertainty) of the belief they express is a relatively unexplored yet intriguing area of linguistic research. The human mind constantly seeks to process as much information as possible for the least possible cognitive effort, yet this is difficult to achieve when reasoning with uncertain knowledge. By exploring the role and characteristics of degree modifiers, my aim is to illuminate how uncertain reasoning permeates many aspects of cognitive linguistic processing and how it relates to the conceptualisation and use of uncertain concepts in language

Computational analysis of superfood representations in news media

What do berries, avocado, quinoa, and ginger have in common? These food items are often regarded as superfoods, a marketing term that overstates the importance of single food items for one’s health and wellbeing. In the present paper, we set out to investigate how purported superfoods are represented in the discourse of online news. We use computational language models to extract the unique topics and terms used to discuss superfoods. Our results show that news coverage is dominated by many specific claims about the healing properties of superfoods. The structural topic model further demonstrates that articles mentioning superfoods are more likely to include topics about a) nutrients, physical appearance, and health in the same context, b) retail strategies, and c) scientific research about the health benefits of superfoods. These results illustrate complex representations of superfoods in news media

A corpus linguistic study of Australian and Chinese health news reporting on salt consumption

Dietary health risks are among the lifestyle-related health challenges seen all over the world, and are connected to every individual's daily behavior. They have motivated a shift from reactive healthcare to proactive health communication and promotion, and therefore have attracted the increased interest of communication professionals and researchers. Salt consumption has become a major dietary risk in the current world, which has been linked to a variety of noncommunicable diseases. China has the world’s third highest mortality rate caused by a diet high in sodium while Australia has made remarkable strides in controlling salt consumption. Exposure to persuasive health communication has been viewed as a significant strategy to influence people's beliefs, attitudes, intentions, or behaviors with the goal of public health intervention. In this regard, the overall purpose of this research is to investigate and compare the linguistic characteristics of health news between the comparable and bilingual Australian-Chinese mass media news corpora. The comparable corpora are self-built, compiled with health-themed news reporting on salt consumption. By analyzing the corpora, this study analyzes communication characteristics from two perspectives, namely information evidentiality and relevance. Analysis will reveal how health messages are presented to be persuasive and effective, which are integral to public health issues and risk perception. Through the application of corpus linguistics and computational linguistics techniques, the similarities and differences of linguistic characteristics will be revealed. This study attempts to advance comparable health communication research. The research findings will send a critical message to communication professionals that health news has the potential to change people’s risk perceptions. This will hopefully have broader implications for the improvement of health news quality