ISSN : 2287-9099
Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s) and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogospherewhen a domain transfer-based self-training strategy was implemented.
We investigated how bibliometric indicators such as publication count and citation count affect the assessment of research performance by computing various bibliometric scores of the works of Korean LIS faculty members and comparing the rankings by those scores. For the study data, we used the publication and citation data of 159 tenuretrack faculty members of Library and Information Science departments in 34 Korean universities. The study results showed correlation between publication count and citation count for authors with many publications but the opposite evidence for authors with few publications. The study results suggest that as authors publish more and more work, citations to their work tend to increase along with publication count. However, for junior faculty members who have not yet accumulated enough publications, citations to their work are of great importance in assessing their research performance. The study data also showed that there are marked differences in the magnitude of citations between papers published in Korean journals and papers published in international journals.
Timely access to quality healthcare information during an outbreak plays an important role in curtailing its spread. The aim of this study was to investigate the information needs and seeking behavior of the general public in Singapore during the H1N1 pandemic. A pre-tested questionnaire was used for data collection. The convenience snowball sampling method was used and 260 working adults and tertiary-level students participated in this study. The most crucial information needs of a majority of the participants were: symptoms of H1N1, causes of the infection, preventive measures, and possible treatments. Data analysis also revealed that mass media such as television, newspapers, and radio were most frequently used for seeking the needed information. The use of human information sources was also quite high while only a small number of the respondents accessed online news and healthcare websites. About three-quarters of the participants indicated that the gathered information helped them to stay vigilant and take necessary precautionary measures. A major problem identified by the participants in using H1N1 information was the lack of understanding of certain terms used in public communications. This paper suggests certain measures for strengthening health information communication during future outbreaks.
Human behaviour normally depends on the environment of the incident and the time of its occurrence. The behaviour of people depends on many factors and these behaviour traits are an important aspect in the Library and Information Science (LIS) field. Hence in this paper an attempt has been made to examine the behaviour traits of LIS students in South India. Out of 400 questionnaires distributed 367 have responded and the response rate is 91.75%. In this survey three aspects comprising student behaviour have been analysed such as Work Environment, Natural Environment, and Social Environment. In the case of Work Environment the respondents were grouped as Workaholic, Impatience, Achievement oriented, Rash nature, and Punctuality. Further, in respect to Natural environment, the respondents are grouped as Complacent, Patience, Easygoing, and Relaxed. Last, the respondents were grouped in the Social Environment as Balancing nature, Magnanimity, Naturalistic, Assertive nature, Dependency, Lucrative, Lonely nature, and Time Based personality. Finally the authors conclude that LIS students need to possess these qualities and behaviours to work in different environments.
Although MAchine Readable Cataloging (MARC) and Functional Requirements for Bibliographic Records (FRBR) are currently the most broadly used bibliographic structures for generating bibliographic data in the library community, each has its own weaknesses in describing information resources in diverse media. If the MARC format could be implemented in a structure that reflects the multi-layered characteristics of FRBR, its use could address current problems and limitations in resource description. The purpose of this research is to propose an alternative approach that can integrate the heterogeneous bibliographic structures of MARC and FRBR through the applications of facet and facet analysis. The proposed faceted data model is expected to function as a conceptual structure that can mediate between MARC data elements and FRBR attributes in order to utilize these structures in a more reliable and comprehensive way.