Exploiting heterogeneous scientific literature networks to combat ranking bias: Evidence from the computational linguistics area.
It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm becaus...
| Publicado en: | Journal of the Association for Information Science & Technology Vol. 67; no. 7; pp. 1679 - 1703 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jul2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115995687&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115995687 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23301635 H6JN jtl: Journal of the Association for Information Science & Technology issn: 23301635 maglogo: N pubinfo: dt: Jul2016 vid: 67 iid: 7 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 115995687 115995687 115995687 10.1002/asi.23463 115995687 ppf: 1679 ppct: 24 formats: tig: atl: Exploiting heterogeneous scientific literature networks to combat ranking bias: Evidence from the computational linguistics area. aug: au: Jiang, Xiaorui Sun, Xiaoping Yang, Zhe Zhuge, Hai Yao, Jianmin affil: School of Information Engineering, Zhejiang University of Technology, No. 288 Liuhe Road, Hangzhou 310023, China sug: subj: Citation Analysis Methods Linguistics Algorithms Human Funding Source Models, Statistical Natural Language Processing ab: It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, Mutual Rank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. Mutual Rank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that Mutual Rank greatly outperforms the state-of-the-art competitors, including Page Rank, HITS, Co Rank, Future Rank, and P- Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by Mutual Rank are also quite reasonable. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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