Chinese word segmentation with a maximum entropy approach

Master'sMASTER OF SCIENC

Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.Comment: 7 pages, 11 figures, accepted by the 2019 World Wide Web Conference (WWW '19

Abstract We participated in the Second International Chinese Word Segmentation

(PKU). Based on a maximum entropy approach, our word segmenter achieved the highest F measure for AS, CITYU, and PKU, and the second highest for MSR. We found that the use of an external dictionary and additional training corpora of different segmentation standards helped to further improve segmentation accuracy. 1 Chinese Word Segmenter The Chinese word segmenter we built is similar to the maximum entropy word segmenter we employed in our previous work (Ng and Low, 2004). Our word segmenter uses a maximum entropy framework (Ratnaparkhi, 1998; Xue and Shen, 2003) and is trained on manually segmented sentences. It classifies each Chinese character given the features derived from its surrounding context. Each Chinese character can be assigned one of four possible boundary tags: s for a character that occurs as a single-character word, b for a character that begins a multi-character (i.e., two or more characters) word, e for a character that ends a multi-character word, and m for a character that is neither the first nor last in a multi-character word. Our implementation used the opennlp maximum entropy package v2.1.0 from sourceforge. 1 1.1 Basic Features The basic features of our word segmenter are similar to our previous work (Ng and Low, 2004)

Enablers and Barriers of a Cross-Cultural Geriatric Education Distance Training Programme: The Singapore-Uganda Experience

Background: By 2050, 80% of the world’s older population will reside in developing countries. There is a need for culturally appropriate training programs to increase awareness of eldercare issues, promote knowledge of how to better allocate resources to geriatric services, and promulgate elder-friendly policies. A monthly distance geriatric education programme between a public hospital in Singapore and health institute in Uganda was implemented. This study explored the enablers and barriers to the delivery of culturally appropriate geriatric education programmes via a videoconferencing platform. Methods: We conducted 12 in-depth interviews with six teachers from Singapore and six learners from Uganda. The interviews were audio-recorded, transcribed and analyzed using an inductive thematic approach to analysis with the aid of the NVivo software. Results: Enablers included inter-personal real-time interactions between teachers and learners whereas misaligned perceptions of cross-cultural differences between Singaporean teachers and Ugandan learners were a barrier. Rapport building, teacher motivation and institutional support were perceived to contribute to the programme’s sustainability. Overall, Ugandan learners perceived that the training improved knowledge, skills, attitude and practice of geriatric care. Participants suggested that future initiatives consider aligning cross-cultural perceptions between partners, conducting a training needs analysis, exploring complementary modes of information dissemination, and allotting time for more interaction, thereby reinforcing mutual sharing. Adequate publicity and appropriate incentivisation may also better sustain the programme. Conclusions: Our findings suggest that cross-cultural training via a videoconferencing platform was feasible. Our results inform planners of future distance educational programmes of how to improve standards of cross-cultural competency and forge promising international partnerships

Paediatric COVID-19 mortality: a database analysis of the impact of health resource disparity

Background The impact of the COVID-19 pandemic on paediatric populations varied between high-income countries (HICs) versus low-income to middle-income countries (LMICs). We sought to investigate differences in paediatric clinical outcomes and identify factors contributing to disparity between countries.Methods The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) COVID-19 database was queried to include children under 19 years of age admitted to hospital from January 2020 to April 2021 with suspected or confirmed COVID-19 diagnosis. Univariate and multivariable analysis of contributing factors for mortality were assessed by country group (HICs vs LMICs) as defined by the World Bank criteria.Results A total of 12 860 children (3819 from 21 HICs and 9041 from 15 LMICs) participated in this study. Of these, 8961 were laboratory-confirmed and 3899 suspected COVID-19 cases. About 52% of LMICs children were black, and more than 40% were infants and adolescent. Overall in-hospital mortality rate (95% CI) was 3.3% [=(3.0% to 3.6%), higher in LMICs than HICs (4.0% (3.6% to 4.4%) and 1.7% (1.3% to 2.1%), respectively). There were significant differences between country income groups in intervention profile, with higher use of antibiotics, antivirals, corticosteroids, prone positioning, high flow nasal cannula, non-invasive and invasive mechanical ventilation in HICs. Out of the 439 mechanically ventilated children, mortality occurred in 106 (24.1%) subjects, which was higher in LMICs than HICs (89 (43.6%) vs 17 (7.2%) respectively). Pre-existing infectious comorbidities (tuberculosis and HIV) and some complications (bacterial pneumonia, acute respiratory distress syndrome and myocarditis) were significantly higher in LMICs compared with HICs. On multivariable analysis, LMIC as country income group was associated with increased risk of mortality (adjusted HR 4.73 (3.16 to 7.10)).Conclusion Mortality and morbidities were higher in LMICs than HICs, and it may be attributable to differences in patient demographics, complications and access to supportive and treatment modalities