Individual Expert Selection and Ranking of Scientific Articles Using Document Length

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of ICT Research and Applications Pub Date : 2019-04-30 DOI:10.5614/ITBJ.ICT.RES.APPL.2019.13.1.3

F. Saputra, Taufik Djatna, L. T. Handoko

{"title":"Individual Expert Selection and Ranking of Scientific Articles Using Document Length","authors":"F. Saputra, Taufik Djatna, L. T. Handoko","doi":"10.5614/ITBJ.ICT.RES.APPL.2019.13.1.3","DOIUrl":null,"url":null,"abstract":"Individual expert selection and ranking is a challenging research topic that has received a lot attention in recent years because of its importance related to referencing experts in particular domains and research fund allocation and management. In this work, scientific articles were used as the most common source for ranking expertise in particular domains. Previous studies only considered title and abstract content using language modeling. This study used the whole content of scientific documents obtained from Aminer citation data. The modified weighted language model (MWLM) is proposed that combines document length and number of citations as prior document probability to improve precision. Also, the author’s dominance in a single document is computed using the Learning-to-Rank (L2R) method. The evaluation results using p@n, MAP, MRR, r-prec, and bpref showed a precision enhancement. MWLM improved the weighted language model (WLM) by p@n (4%), MAP (22.5%), and bpref (1.7%). MWLM also improved the precision of a model that used author dominance by MAP (4.3%), r-prec (8.2%), and bpref (2.1%).","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of ICT Research and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5614/ITBJ.ICT.RES.APPL.2019.13.1.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Individual expert selection and ranking is a challenging research topic that has received a lot attention in recent years because of its importance related to referencing experts in particular domains and research fund allocation and management. In this work, scientific articles were used as the most common source for ranking expertise in particular domains. Previous studies only considered title and abstract content using language modeling. This study used the whole content of scientific documents obtained from Aminer citation data. The modified weighted language model (MWLM) is proposed that combines document length and number of citations as prior document probability to improve precision. Also, the author’s dominance in a single document is computed using the Learning-to-Rank (L2R) method. The evaluation results using p@n, MAP, MRR, r-prec, and bpref showed a precision enhancement. MWLM improved the weighted language model (WLM) by p@n (4%), MAP (22.5%), and bpref (1.7%). MWLM also improved the precision of a model that used author dominance by MAP (4.3%), r-prec (8.2%), and bpref (2.1%).

查看原文本刊更多论文

使用文档长度的科学文章的个人专家选择和排名

个人专家的选择与排名是近年来备受关注的一个具有挑战性的研究课题，因为它关系到参考特定领域的专家和研究经费的分配与管理。在这项工作中，科学文章被用作对特定领域的专业知识进行排名的最常见来源。以往的研究使用语言建模只考虑标题和抽象内容。本研究使用了从Aminer引文数据中获得的科学文献的全部内容。提出了一种改进的加权语言模型(MWLM)，该模型将文档长度和引用次数作为先验文档概率，以提高精度。此外，作者在单个文档中的支配地位是使用学习排序(L2R)方法计算的。使用p@n、MAP、MRR、r-prec和bpref评价结果显示精度提高。MWLM改进了加权语言模型(WLM) p@n(4%)、MAP(22.5%)和bpref(1.7%)。MWLM还通过MAP(4.3%)、r-prec(8.2%)和bpref(2.1%)提高了使用作者优势的模型的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of ICT Research and Applications COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

1.60

自引率

0.00%

发文量

审稿时长

24 weeks

期刊介绍： Journal of ICT Research and Applications welcomes full research articles in the area of Information and Communication Technology from the following subject areas: Information Theory, Signal Processing, Electronics, Computer Network, Telecommunication, Wireless & Mobile Computing, Internet Technology, Multimedia, Software Engineering, Computer Science, Information System and Knowledge Management. Authors are invited to submit articles that have not been published previously and are not under consideration elsewhere.