An entropy-based query expansion approach for learning researchers' dynamic information needs

I-Chin Wu; Guan-Wei Chen; Jia-Lien Hsu; Chun-Yu Lin

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An entropy-based query expansion approach for learning researchers' dynamic information needs

机译：基于熵的查询扩展方法，用于学习研究人员的动态信息需求

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Literature survey is one of the most important steps in the process of academic research, allowing researchers to explore and understand topics. However, novice researchers without sufficient prior knowledge lack the skills to determine proper keywords for searching topics of choice. To tackle this problem, we propose an entropy-based query expansion with a reweighting (E_QE) approach to revise queries during the iterative retrieval process. We designed a series of experiments that consider the researcher's changing information needs during task execution. Three topic change situations are considered in this work: minor, moderate and dramatic topic changes. The simulation-based pseudo-relevance feedback technique is applied during the search process to evaluate the effectiveness of the proposed approach without the intervention of human effort. We measured the effectiveness of the TFIDF and E_QE approaches for different types of topic change situations. The results show that the proposed E_QE approach achieves better search results than the TFIDF, helping researchers to revise queries. The results also confirm that the E_QE approach is effective when considering the relevant and irrelevant pages during the relevance feedback process at different levels of topic change.

机译：文献调查是学术研究过程中最重要的步骤之一，它使研究人员能够探索和理解主题。但是，没有足够先验知识的新手研究人员缺乏确定适当关键词以搜索所选主题的技能。为解决此问题，我们提出了一种基于熵的查询扩展，并采用重加权（E_QE）方法在迭代检索过程中修改查询。我们设计了一系列实验，这些研究考虑了研究人员在任务执行过程中不断变化的信息需求。在这项工作中考虑了三种主题更改情况：较小，中等和剧烈的主题更改。在搜索过程中应用了基于仿真的伪相关反馈技术，以评估该方法的有效性，而无需人工干预。我们测量了TFIDF和E_QE方法对不同类型的主题更改情况的有效性。结果表明，提出的E_QE方法比TFIDF具有更好的搜索结果，有助于研究人员修改查询。结果还证实，在不同主题更改级别的相关性反馈过程中，考虑相关页面和不相关页面时，E_QE方法是有效的。

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《Knowledge-Based Systems》 |2013年第11期|133-146|共14页
作者
I-Chin Wu; Guan-Wei Chen; Jia-Lien Hsu; Chun-Yu Lin;
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作者单位

Department of Information Management, Fu-Jen Catholic University, 510 Chung Cheng Rd, Xinzhuang Dist, Xinbei City 24205, Taiwan;

Department of Information Management, Fu-Jen Catholic University, 510 Chung Cheng Rd, Xinzhuang Dist, Xinbei City 24205, Taiwan;

Department of Computer Science and Information Engineering, Fu-Jen Catholic University, 510 Chung Cheng Rd, Xinzhuang Dist, Xinbei City 24205, Taiwan;

Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan;

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原文格式 PDF
正文语种 eng
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关键词
Query expansion; Pseudo-relevance feedback; Term weighting; Topic change; Entropy;

机译：查询扩展;伪相关反馈;期限加权;主题变更;熵;

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机译：研究人员提交了专利申请“用于查询扩展的机器翻译”以供批准
2. Query Expansion with Enhanced-BM25 Approach for Improving the Search Query Performance on Clustered Biomedical Literature Retrieval [J] . Thayyaba Khatoon, A. Govardhan Journal of digital information management . 2018,第2期

机译：使用增强型BM25的查询扩展，可提高生物医学文献检索中的查询性能
3. Using Query Expansion In Graph-based Approach For Query-focused Multi-document Summarization [J] . Lin Zhao, Lide Wu, Xuanjing Huang Information Processing & Management . 2009,第1期

机译：在基于图的方法中使用查询扩展进行以查询为中心的多文档摘要
4. On Learning Researchers' Dynamic Information Needs: An Entropy-Based Query Expansion Approach [C] . Wu I-Chin, Lin Chun-Yu, Chen Guan-Wei, IEEE/WIC/ACM International Conference on Web Intelligence;WI 2012;IAT 2012;IEEE/WIC/ACM International Conference on Intelligent Agent Technology;ODMWI 2012;International Workshop on Optimization-based Data Mining and Web Intelligence;BI 2012;International Workshop on Behavior Informatics;IWI-2012;TF'12;International Workshop on Intelligent Web Interaction;NLPOE 2012;International Workshop on Tourism Facilities;NiCaM-WI 2012;WPRSM 2012;International Workshop on Natural Language Processing and Ontology Engineering;WIRSS;International Workshop on Nature-Inspired Computing and Metaheuristics for Web Intelligence;WISS 2012;International Workshop on Web Personalization, Recommender Systems and Social Media;International Workshop on Web Information Retrieval Support Systems;International Symposium on Web Intelligent Systems Services;Combined Workshop on Cross-Cultural and Cross-Linguistic Semantic Web and Software Agent Teamwork for the Semantic Web;International Workshop on Social Networks and Data Processing;SNDP 2012;International Symposium on the Intelligent Campus;IC'12;International Workshop on Green Computing and Sustainable Society;GCSS . 2012

机译：学习研究人员的动态信息需求：基于熵的查询扩展方法
5. Text clustering and active learning using a LSI subspace signature model and query expansion. [D] . Zhu, Weizhong. 2009

机译：使用LSI子空间签名模型和查询扩展进行文本聚类和主动学习。
6. Does Query Expansion Limit Our Learning? A Comparison of Social-Based Expansion to Content-Based Expansion for Medical Queries on the Internet [O] . Christopher Pentoney, Jeff Harwell, Gondy Leroy 2014

机译：查询扩展是否会限制我们的学习？互联网上医疗查询的基于社交的扩展与基于内容的扩展的比较
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

An entropy-based query expansion approach for learning researchers' dynamic information needs

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