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window.loswp.work = {"work":{"id":2788010,"created_at":"2013-03-03T15:08:48.323-08:00","from_world_paper_id":null,"updated_at":"2023-06-12T16:42:18.200-07:00","_data":{"abstract":"Results from research in information retrieval suggest that significant improvements in retrieval effectiveness could be obtained by combining results from multiple index representations and query strategies. Recently, an inference network based probabilistic retrieval model has been proposed, which views information retrieval as an evidential reasoning process in which multiple sources of evidence about document and query content are combined to estimate the relevance probabilities.","journal_name":"Journal of the American society for information science","publication_date":"1995,,"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"low","language":"vi","title":"Combining automatic and manual index representations in probabilistic retrieval","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [1389]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":30740448,"attachmentType":"pdf"}"><img alt="First page of “Combining automatic and manual index representations in probabilistic retrieval”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/30740448/mini_magick20190426-29272-z2jtwd.png?1556319636" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Combining automatic and manual index representations in probabilistic retrieval</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="1389" href="https://umass.academia.edu/WBruceCroft"><img alt="Profile image of W. Bruce Croft" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/1389/573/687/s65_w._bruce.croft.jpg" />W. Bruce Croft</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">1995</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">21 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 2788010; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=2788010"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Results from research in information retrieval suggest that significant improvements in retrieval effectiveness could be obtained by combining results from multiple index representations and query strategies. Recently, an inference network based probabilistic retrieval model has been proposed, which views information retrieval as an evidential reasoning process in which multiple sources of evidence about document and query content are combined to estimate the relevance probabilities.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":30740448,"attachmentType":"pdf","workUrl":"https://www.academia.edu/2788010/Combining_automatic_and_manual_index_representations_in_probabilistic_retrieval"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":30740448,"attachmentType":"pdf","workUrl":"https://www.academia.edu/2788010/Combining_automatic_and_manual_index_representations_in_probabilistic_retrieval"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Kỹ thuật CF dựa trên giả thiết rằng những người dùng (user) có cùng sở thích thì sẽ quan tâm một tập item tương tự. Phương pháp phân cụm lọc cộng tác (Iterative Clustered CF -ICCF) và lặp cộng tác tối ưu trọng số sử dụng thuật toán PSO (PSO-Feature Weighted) thể hiện tính hiệu quả cho hệ gợi ý mà giá trị đánh giá thuộc trong tập {1, 2,…, 5}. Tuy nhiên, các kỹ thuật đó không thể trực tiếp áp dụng cho các hệ thống gợi ý trong thực tế mà giá trị đánh giá trong tập {0, 1}. Do vậy, bài báo này đề xuất việc cải tiến hai phương pháp ICCF và PSO-Feature Weighted để có thể áp dụng được cho các hệ gợi ý mà giá trị đánh giá thuộc tập {0, 1}. Kết quả thực nghiệm của hai phương pháp mà chúng tôi đưa ra áp dụng trên bộ dữ liệu hệ gợi ý công việc cho thấy độ chính xác mô hình dự đoán có cải thiện rõ rệt so với phương pháp CF truyền thống đồng thời cũng giải quyết được vấn đề dữ liệu thưa mà phương pháp CF thường gặp phải.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Hệ Thống Gợi Ý Sử Dụng Thuật Toán Tối Ưu Bầy Đàn","attachmentId":114261730,"attachmentType":"pdf","work_url":"https://www.academia.edu/118695408/H%E1%BB%87_Th%E1%BB%91ng_G%E1%BB%A3i_%C3%9D_S%E1%BB%AD_D%E1%BB%A5ng_Thu%E1%BA%ADt_To%C3%A1n_T%E1%BB%91i_%C6%AFu_B%E1%BA%A7y_%C4%90%C3%A0n","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/118695408/H%E1%BB%87_Th%E1%BB%91ng_G%E1%BB%A3i_%C3%9D_S%E1%BB%AD_D%E1%BB%A5ng_Thu%E1%BA%ADt_To%C3%A1n_T%E1%BB%91i_%C6%AFu_B%E1%BA%A7y_%C4%90%C3%A0n"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="123992495" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/123992495/From_Artificial_Intelligence_to_Dependability_Modeling_and_Analysis_with_Bayesian_Networks">From Artificial Intelligence to Dependability: Modeling and Analysis with Bayesian Networks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="69563648" href="https://independent.academia.edu/LuigiPortinale">Luigi Portinale</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Series on Quality, Reliability and Engineering Statistics, 2005</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"From Artificial Intelligence to Dependability: Modeling and Analysis with Bayesian Networks","attachmentId":118301431,"attachmentType":"pdf","work_url":"https://www.academia.edu/123992495/From_Artificial_Intelligence_to_Dependability_Modeling_and_Analysis_with_Bayesian_Networks","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/123992495/From_Artificial_Intelligence_to_Dependability_Modeling_and_Analysis_with_Bayesian_Networks"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="50763928" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/50763928/Function_approximation_with_spiked_random_networks">Function approximation with spiked random networks</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34002191" href="https://iitis.academia.edu/ErolGelenbe">Erol Gelenbe</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Transactions on Neural Networks, 1999</p><div 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data-collection-position="3" data-entity-id="120324309" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/120324309/%C4%90i%E1%BB%83m_Danh_B%E1%BA%B1ng_M%E1%BA%B7t_Ng%C6%B0%E1%BB%9Di_V%E1%BB%9Bi_%C4%90%E1%BA%B7c_Tr%C6%B0ng_Gist_V%C3%A0_M%C3%A1y_H%E1%BB%8Dc_V%C3%A9ct%C6%A1_H%E1%BB%97_Tr%E1%BB%A3">Điểm Danh Bằng Mặt Người Với Đặc Trưng Gist Và Máy Học Véctơ Hỗ Trợ</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="287745123" href="https://independent.academia.edu/16Tr%E1%BA%A7nNguy%E1%BB%85nMinhTh%C6%B0">16.Trần Nguyễn Minh Thư</a></div><p class="ds-related-work--metadata ds2-5-body-xs">FAIR - NGHIÊN CỨU CƠ BẢN VÀ ỨNG DỤNG CÔNG NGHỆ THÔNG TIN - 2017, 2017</p><p class="ds-related-work--abstract ds2-5-body-sm">Trong bài viết này, chúng tôi đề xuất hệ thống điểm danh bằng mặt người với máy học véctơ hỗ trợ (Support Vector Machines-SVM) sử dụng đặc trưng GIST. Hệ thống điểm danh thực hiện hai bước chính là định vị khuôn mặt trong ảnh thu được từ camera và định danh đối tượng từ ảnh khuôn mặt. Bước định vị khuôn mặt được thực hiện dựa trên các đặc trưng Haar-like kết hợp với mô hình phân tầng (Cascade of Boosted Classifiers-CBC). Chúng tôi đề xuất huấn luyện mô hình máy học SVM sử dụng đặc trưng GIST để thực hiện định danh đối tượng từ ảnh khuôn mặt. Kết quả thực nghiệm trên tập dữ liệu gồm 6722 ảnh của 132 đối tượng là những sinh viên Khoa CNTT-TT, Trường Đại học Cần Thơ cho thấy máy học SVM sử dụng đặc trưng GIST đạt đến 99.29% độ chính xác trên tập kiểm tra, cao hơn khi so với mô hình mạng nơron tích chập (Convolutional Neural Network-CNN), máy học SVM sử dụng mô hình túi từ (Bag-of-Words-BoW) của đặc trưng SIFT (Scale-Invariant Feature Transform), Bayes thơ ngây với láng giềng gần nhất (Naïve Bayes Nearest Neighbor-NBNN) sử dụng đặc trưng SIFT có độ chính xác lần lượt là 96.88%, 97.54% và 98.88%. Từ khóa: Nhận dạng mặt người, đặc trưng GIST, máy học véctơ hỗ trợ (SVM).</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Điểm Danh Bằng Mặt Người Với Đặc Trưng Gist Và Máy Học Véctơ Hỗ Trợ","attachmentId":115512396,"attachmentType":"pdf","work_url":"https://www.academia.edu/120324309/%C4%90i%E1%BB%83m_Danh_B%E1%BA%B1ng_M%E1%BA%B7t_Ng%C6%B0%E1%BB%9Di_V%E1%BB%9Bi_%C4%90%E1%BA%B7c_Tr%C6%B0ng_Gist_V%C3%A0_M%C3%A1y_H%E1%BB%8Dc_V%C3%A9ct%C6%A1_H%E1%BB%97_Tr%E1%BB%A3","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" 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href="https://independent.academia.edu/08Tr%E1%BA%A7n%C4%90%C3%ACnhKhang">08.Trần Đình Khang</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Computer Science and Cybernetics, 2016</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Xây dựng hệ trợ giúp tính toán thông minh","attachmentId":114261628,"attachmentType":"pdf","work_url":"https://www.academia.edu/118695413/X%C3%A2y_d%E1%BB%B1ng_h%E1%BB%87_tr%E1%BB%A3_gi%C3%BAp_t%C3%ADnh_to%C3%A1n_th%C3%B4ng_minh","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/118695413/X%C3%A2y_d%E1%BB%B1ng_h%E1%BB%87_tr%E1%BB%A3_gi%C3%BAp_t%C3%ADnh_to%C3%A1n_th%C3%B4ng_minh"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="118243099" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/118243099/Analyzing_individual_based">Analyzing individual-based</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="42476322" href="https://esma.academia.edu/NabilMabrouk">Nabil Mabrouk</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2012</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Analyzing individual-based","attachmentId":113915859,"attachmentType":"pdf","work_url":"https://www.academia.edu/118243099/Analyzing_individual_based","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/118243099/Analyzing_individual_based"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="52829473" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/52829473/A_Path_sequence_Based_Discrimination_for_Subtree_Matching_in_Approximate_XML_Joins">A Path-sequence Based Discrimination for Subtree Matching in Approximate XML Joins</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="37267579" href="https://independent.academia.edu/HaruoYokota">Haruo Yokota</a></div><p class="ds-related-work--metadata ds2-5-body-xs">22nd International Conference on Data Engineering Workshops (ICDEW'06), 2006</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Path-sequence Based Discrimination for Subtree Matching in Approximate XML Joins","attachmentId":69904924,"attachmentType":"pdf","work_url":"https://www.academia.edu/52829473/A_Path_sequence_Based_Discrimination_for_Subtree_Matching_in_Approximate_XML_Joins","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/52829473/A_Path_sequence_Based_Discrimination_for_Subtree_Matching_in_Approximate_XML_Joins"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="51568674" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/51568674/Analisis_Investasi_dan_Pemilihan_Portofolio_Optimal_pada_Indeks_Saham_Kompas_100_dengan_Menggunakan_Single_Index_Model">Analisis Investasi dan Pemilihan Portofolio Optimal pada Indeks Saham Kompas-100 dengan Menggunakan Single Index Model</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="8106433" href="https://pascaunesa.academia.edu/NadiaAsandimitra">Nadia Asandimitra</a></div><p class="ds-related-work--metadata ds2-5-body-xs">BISMA (Bisnis dan Manajemen)</p><p class="ds-related-work--abstract ds2-5-body-sm">A stock investment is one of alternatives that have a bright prospect in the futures. However, it is necessary to manage the risks, which is an important of choosing share intended. Up to now a relevan classic says “Don’t put all of your eggs in one basket”. Therefore the share diversification is necessary as an investment choise to form a portfolio. The main purpose of this research are the ascertain the optimal portfolio by using single index model. Population to be chosen in the study is firms listed on Kompas-100. However, the simple included are only 33 firms that present continuously simultan on Kompas-100. This research results showed that there were ten stocks of portfolio candidates from thirty-three stocks researched with the cut-off-point (C*) of 0,016495. And ten of stocks which have the biggest excess return to beta (ERB) make up the optimal portfolio. Allocation of these funds comprises 1,1365% for Astra Agro Lestari Inc stock (AALI), 14,3439% for Astra International I...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Analisis Investasi dan Pemilihan Portofolio Optimal pada Indeks Saham Kompas-100 dengan Menggunakan Single Index Model","attachmentId":69237317,"attachmentType":"pdf","work_url":"https://www.academia.edu/51568674/Analisis_Investasi_dan_Pemilihan_Portofolio_Optimal_pada_Indeks_Saham_Kompas_100_dengan_Menggunakan_Single_Index_Model","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/51568674/Analisis_Investasi_dan_Pemilihan_Portofolio_Optimal_pada_Indeks_Saham_Kompas_100_dengan_Menggunakan_Single_Index_Model"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="101391574" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/101391574/T%E1%BB%95ng_H%E1%BB%A3p_M%E1%BB%99t_S%E1%BB%91_Ph%C6%B0%C6%A1ng_Ph%C3%A1p_H%E1%BB%8Dc_S%C3%A2u_%C3%81p_D%E1%BB%A5ng_V%C3%A0o_B%C3%A0i_To%C3%A1n_L%E1%BB%B1a_Ch%E1%BB%8Dn_C%C3%A2u_Tr%E1%BA%A3_L%E1%BB%9Di_Trong_H%E1%BB%87_Th%E1%BB%91ng_H%E1%BB%8Fi_%C4%90%C3%A1p_C%E1%BB%99ng_%C4%90%E1%BB%93ng">Tổng Hợp Một Số Phương Pháp Học Sâu Áp Dụng Vào Bài Toán Lựa Chọn Câu Trả Lời Trong Hệ Thống Hỏi Đáp Cộng Đồng</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="268697077" href="https://independent.academia.edu/H%C3%A0Thanh349">Hà Thanh</a></div><p class="ds-related-work--metadata ds2-5-body-xs">TNU Journal of Science and Technology, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Bài toán tìm câu trả lời (còn gọi là bài toán lựa chọn câu trả lời hay tìm câu trả lời tốt nhất) là một bài toán chính trong hệ thống hỏi đáp. Khi một câu hỏi được đăng lên forum sẽ có nhiều người tham gia trả lời câu hỏi. Bài toán lựa chọn câu trả lời với mục đích thực hiện sắp xếp các câu trả lời theo mức độ liên quan tới câu hỏi. Những câu trả lời nào đúng nhất sẽ được đứng trước các câu trả lời kém liên quan hơn. Trong những năm gần đây, rất nhiều mô hình học sâu được đề xuất sử dụng vào nhiều bài toán xử lý ngôn ngữ tự nhiên (NLP) trong đó có bài toán lựa chọn câu trả lời trong hệ thống hỏi đáp nói chung và trong hệ thống hỏi đáp cộng đồng (CQA) nói riêng. Hơn nữa, các mô hình được đề xuất lại thực hiện trên các tập dữ liệu khác nhau. Vì vậy, trong bài báo này, chúng tôi tiến hành tổng hợp và trình bày một số mô hình học sâu điển hình khi áp dụng vào bài toán tìm câu trả lời đúng trong hệ thống hỏi đáp và phân tích một số thách thức trên các tập dữ liệu cho bài toán trên hệ thố...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Tổng Hợp Một Số Phương Pháp Học Sâu Áp Dụng Vào Bài Toán Lựa Chọn Câu Trả Lời Trong Hệ Thống Hỏi Đáp Cộng Đồng","attachmentId":101946096,"attachmentType":"pdf","work_url":"https://www.academia.edu/101391574/T%E1%BB%95ng_H%E1%BB%A3p_M%E1%BB%99t_S%E1%BB%91_Ph%C6%B0%C6%A1ng_Ph%C3%A1p_H%E1%BB%8Dc_S%C3%A2u_%C3%81p_D%E1%BB%A5ng_V%C3%A0o_B%C3%A0i_To%C3%A1n_L%E1%BB%B1a_Ch%E1%BB%8Dn_C%C3%A2u_Tr%E1%BA%A3_L%E1%BB%9Di_Trong_H%E1%BB%87_Th%E1%BB%91ng_H%E1%BB%8Fi_%C4%90%C3%A1p_C%E1%BB%99ng_%C4%90%E1%BB%93ng","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/101391574/T%E1%BB%95ng_H%E1%BB%A3p_M%E1%BB%99t_S%E1%BB%91_Ph%C6%B0%C6%A1ng_Ph%C3%A1p_H%E1%BB%8Dc_S%C3%A2u_%C3%81p_D%E1%BB%A5ng_V%C3%A0o_B%C3%A0i_To%C3%A1n_L%E1%BB%B1a_Ch%E1%BB%8Dn_C%C3%A2u_Tr%E1%BA%A3_L%E1%BB%9Di_Trong_H%E1%BB%87_Th%E1%BB%91ng_H%E1%BB%8Fi_%C4%90%C3%A1p_C%E1%BB%99ng_%C4%90%E1%BB%93ng"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="114363075" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/114363075/Representaci%C3%B3n_e_interoperabilidad_de_im%C3%A1genes_biom%C3%A9tricas">Representación e interoperabilidad de imágenes biométricas</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="61819338" href="https://independent.academia.edu/CarlosAlvez1">Carlos Alvez</a></div><p class="ds-related-work--metadata ds2-5-body-xs">XVII Workshop de Investigadores en Ciencias de la Computación (Salta, 2015), 2015</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Representación e interoperabilidad de imágenes biométricas","attachmentId":111081246,"attachmentType":"pdf","work_url":"https://www.academia.edu/114363075/Representaci%C3%B3n_e_interoperabilidad_de_im%C3%A1genes_biom%C3%A9tricas","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/114363075/Representaci%C3%B3n_e_interoperabilidad_de_im%C3%A1genes_biom%C3%A9tricas"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":30740448,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":30740448,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_30740448" style="display: none"><div class="scribd--being-converted-container">This document is currently being converted. 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