Biomedical concept extraction based Information Retrieval model: application on the MeSH

Social media users can be influenced directly by their close relationships, such as their friends, family, and colleagues. They can also be influenced by those who follow them through shared information, goals, news, and opinions. Generally, an influencer is someone who entices an influence to do the same action, make the same decision, or change their behavior. He can also communicate information, ideas, and thoughts to multiple users. There are many tools to identify influencers. It can not be found simply through their big follower number or their shared media number. Thus, influencer identification is one of the essential tasks in social media research. Several approaches and metrics have been proposed in the literature to identify influencers. In this article, we explored the issue of identifying social media influencers while providing a generic view of social media influence. First, we presented a literature synthesis on the influence of social media. Then, we categorized the works and illustrated the leading solutions in literature to identify influencers in social media. A discussion and suggestions for potential future directions in this area accompanied this presentation. We believe these briefings are critical to resolving the issue discussed in this article.

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Employing Semantic Context for Sparse Information Extraction Assessment

Wang

et al. 2018

ACM Trans. Knowl. Discov. Data

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A huge amount of texts available on the World Wide Web presents an unprecedented opportunity for information extraction (IE). One important assumption in IE is that frequent extractions are more likely to be correct. Sparse IE is hence a challenging task because no matter how big a corpus is, there are extractions supported by only a small amount of evidence in the corpus. However, there is limited research on sparse IE, especially in the assessment of the validity of sparse IEs. Motivated by this, we introduce a lightweight, explicit semantic approach for assessing sparse IE. 1 We first use a large semantic network consisting of millions of concepts, entities, and attributes to explicitly model the context of any semantic relationship. Second, we learn from three semantic contexts using different base classifiers to select an optimal classification model for assessing sparse extractions. Finally, experiments show that as compared with several state-of-the-art approaches, our approach can significantly improve the F -score in the assessment of sparse extractions while maintaining the efficiency.

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Biomedical concept extraction based Information Retrieval model: application on the MeSH

Cited by 3 publications

References 14 publications

Hybridization of an Index Based on Concept Lattice with a Terminology Extraction Model for Semantic Information Retrieval Guided by WordNet

Hybridization of an Index Based on Concept Lattice with a Terminology Extraction Model for Semantic Information Retrieval Guided by WordNet

A Survey on Social Media Influence Environment and Influencers Identification

Employing Semantic Context for Sparse Information Extraction Assessment

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