a b s t r a c tTraditional clustering algorithms do not consider the semantic relationships among words so that cannot accurately represent the meaning of documents. To overcome this problem, introducing semantic information from ontology such as WordNet has been widely used to improve the quality of text clustering. However, there still exist several challenges, such as synonym and polysemy, high dimensionality, extracting core semantics from texts, and assigning appropriate description for the generated clusters. In this paper, we report our attempt towards integrating WordNet with lexical chains to alleviate these problems. The proposed approach exploits ontology hierarchical structure and relations to provide a more accurate assessment of the similarity between terms for word sense disambiguation. Furthermore, we introduce lexical chains to extract a set of semantically related words from texts, which can represent the semantic content of the texts. Although lexical chains have been extensively used in text summarization, their potential impact on text clustering problem has not been fully investigated. Our integrated way can identify the theme of documents based on the disambiguated core features extracted, and in parallel downsize the dimensions of feature space. The experimental results using the proposed framework on reuters-21578 show that clustering performance improves significantly compared to several classical methods.
Information plays an important role in meeting the quantitative and qualitative goals of agriculture in the 21st century. As an emerging economy in a developing continent, China has already made many interventions to use information technology to support agricultural development. However, information service in some rural areas is still severely limited. The overall impact of the changing information environment on the farmers’ information needs and access channels has not been fully studied. Thus, this study systematically investigates the characteristics of the information needs and channels of farmers in Guangdong, China. We have collected 4006 questionnaire samples and used correlation analysis to explore the relationships between farmers’ information needs and access channel preferences. The results indicate that individual characteristic factors, social factors and family factors have different degrees of influence on farmers’ information needs and access channel preferences. These findings can provide a reference for information construction in the rural areas of Guangdong Province and thus promote its economic development. This study can also provide useful insights for policy-makers and researchers from other developing countries to formulate implementation plans to promote agricultural development.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.