NEMO: Extraction and normalization of organization names from PubMed affiliations

Background: We are witnessing an exponential increase in biomedical research citations in PubMed. However, translating biomedical discoveries into practical treatments is estimated to take around 17 years, according to the 2000 Yearbook of Medical Informatics, and much information is lost during this transition. Pharmaceutical companies spend huge sums to identify opinion leaders and centers of excellence. Conventional methods such as literature search, survey, observation, self‐identification, expert opinion, and sociometry not only need much human effort, but are also non‐comprehensive. Such huge delays and costs can be reduced by “connecting those who produce the knowledge with those who apply it”. A humble step in this direction is large‐scale discovery of persons and organizations involved in specific areas of research. This can be achieved by automatically extracting and disambiguating author names and affiliation strings retrieved through Medical Subject Heading (MeSH) terms and other keywords associated with articles in PubMed. In this study, we propose NEMO (Normalization Engine for Matching Organizations), a system for extracting organization names from the affiliation strings provided in PubMed abstracts, building a thesaurus (list of synonyms) of organization names, and subsequently normalizing them to a canonical organization name using the thesaurus. Results: We used a parsing process that involves multi‐layered rule matching with multiple dictionaries. The normalization process involves clustering based on weighted local sequence alignment metrics to address synonymy at word level, and local learning based on finding connected components to address synonymy. The graphical user interface and java client library of NEMO are available at http://lnxnemo.sourceforge.net. Conclusion: NEMO associates each biomedical paper and its authors with a unique organization name and the geopolitical location of that organization. This system provides more accurate information about organizations than the raw affiliation strings provided in PubMed abstracts. It can be used for : a) bimodal social network analysis that evaluates the research relationships between individual researchers and their institutions; b) improving author name disambiguation; c) augmenting National Library of Medicine (NLM)’s Medical Articles Record System (MARS) system for correcting errors due to OCR on affiliation strings that are in small fonts; and d) improving PubMed citation indexing strategies (authority control) based on normalized organization name and country

Adding Context to Automated Text Input Error Analysis with Reference to Understanding How Children Make Typing Errors

Despite the enormous body of literature studying the typing errors of adults, children's typing errors remain an understudied area. It is well known in the field of Child-Computer Interaction that children are not 'little adults'. This means findings regarding how adults make typing mistakes cannot simply be transferred into how children make typing errors, without first understanding the differences. To understand how children differ from adults in the way they make typing mistakes, typing data were gathered from both children and adults. It was important that the data collected from the contrasting participant groups were comparable. Various methods of collecting typing data from adults were reviewed for suitability with children. Several issues were identified that could create a bias towards the adults. To resolve these issues, new tools and methods were designed, such as a new phrase set, a new data collector and new computer experience questionnaires. Additionally, there was a lack of an analysis method of typing data suitable for use with both children and adults. A new categorisation method was defined based on typing errors made by both children and adults. This categorisation method was then adapted into a Java program, which dramatically reduced the time required to carry out typing categorisation. Finally, in a large study, typing data collected from 231 primary school children, aged between 7 and 10 years, and 229 undergraduate computing students were analysed. Grouping the typing errors according to the context in which they occurred allowed for a much more detailed analysis than was possible with error rates. The analysis showed children have a set of errors they made frequently that adults rarely made. These errors that are specific to children suggest that differences exist between the ways the two groups make typing errors. This finding means that children's typing errors should be studied in their own right

Effects of word processing on text revision

Revising is an evaluating and editing process that is an essential part of text production. Is text revising facilitated by the use of word processors? After examining the related research, it is difficult to conclude with certainty that the use of word processors is always effective in improving writers' revising skills, or that their use necessarily leads to the production of higher quality texts. Their effectiveness depends on a large number of parameters (computer equipment, writing skills, task execution conditions) which psychologists are now starting to measure

Close Copy Speech Synthesis for Speech Perception Testing

The present study is concerned with developing a speech synthesis subcomponent for perception testing in the context of evaluating cochlear implants in children. We provide a detailed requirements analysis, and develop a strategy for maximally high quality speech synthesis using Close Copy Speech synthesis techniques with a diphone based speech synthesiser, MBROLA. The close copy concept used in this work defines close copy as a function from a pair of speech signal recording and a phonemic annotation aligned with the recording into the pronunciation specification interface of the speech synthesiser. The design procedure has three phases: Manual Close Copy Speech (MCCS) synthesis as a ?best case gold standard?, in which the function is implemented manually as a preliminary step; Automatic Close Copy Speech (ACCS) synthesis, in which the steps taken in manual transformation are emulated by software; finally, Parametric Close Copy Speech (PCCS) synthesis, in which prosodic parameters are modifiable while retaining the diphones. This contribution reports on the MCCS and ACCS synthesis phases

The National Singing Programme for primary schools in England: an initial baseline study

The ‘Sing Up’ National Singing Programme for Primary schools in England was launched in November 2007 under the UK Government’s ‘Music Manifesto’. ‘Sing Up’ is a four-year programme whose overall aim is to raise the status of singing and increase opportunities for children throughout the country to enjoy singing as part of their everyday lives, in and out of school. As part of the Programme’s research evaluation, a key focus has been to build an initial picture of singing in Primary schools across England. This information could then be used as a ‘baseline’ by which the programme’s subsequent impact could be judged, including ‘before’ and ‘after’ measures of schools that receive particular ‘Sing Up’ input. This paper reports an overview of key outcomes of first five months of baseline profiling (October, 2007 to February 2008), embracing analyses of the singing behaviours of 3,472 children in 76 Primary schools. These findings are complimented by additional analyses of children’s views on singing in and out of school; and the self-efficacy of their class teachers’ (n=90), both as singers and as teachers of singing