VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T02:34:06ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2016-12-23T04:04:21Z2016-12-23T04:04:21Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2296This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/22962016-12-23T04:04:21ZWeb Search Clustering and Labeling with Hidden TopicsCam Tu NguyenXuan Hieu Phanhieupx@vnu.edu.vnSusumu HoriguchiThu Trang NguyenQuang Thuy Hathuyhq@vnu.edu.vn2016-12-23T04:04:07Z2016-12-23T04:04:07Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2297This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/22972016-12-23T04:04:07ZLearning to classify short and sparse text & web with hidden topics from large-scale data collectionsXuan Hieu Phanhieupx@vnu.edu.vnLe Minh NguyenSusumu Horiguchi2016-12-23T04:03:50Z2016-12-23T04:03:50Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2298This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/22982016-12-23T04:03:50ZMatching and Ranking with Hidden Topics towards Online Contextual AdvertisingDieu Thu LeCam Tu NguyenQuang Thuy Hathuyhq@vnu.edu.vnXuan Hieu Phanhieupx@vnu.edu.vnSusumu Horiguchi2016-12-23T04:03:22Z2016-12-23T04:03:22Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2300This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/23002016-12-23T04:03:22ZAn Efficient Feature Selection Using Hidden Topic in Text CategorizationZhiwei ZhangXuan Hieu Phanhieupx@vnu.edu.vnSusumu Horiguchi2016-12-23T04:03:14Z2016-12-23T04:03:14Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2301This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/23012016-12-23T04:03:14ZSemantic Analysis of Entity Contexts towards Open Named Entity Classification on the WebXuan Hieu Phanhieupx@vnu.edu.vnSusumu HoriguchiLe Minh NguyenCam Tu Nguyen2016-12-23T04:03:01Z2016-12-23T04:03:01Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2302This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/23022016-12-23T04:03:01ZA new sentence reduction technique based on a decision tree modelLe Minh NguyenXuan Hieu Phanhieupx@vnu.edu.vnSusumu HoriguchiAkira Shimazu2016-12-23T04:02:45Z2016-12-23T04:02:45Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/2303This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/23032016-12-23T04:02:45ZHigh-Performance Training of Conditional Random Fields for Large-scale Applications of Labeling Sequence DataXuan Hieu Phanhieupx@vnu.edu.vnLe Minh NguyenYasushi InoguchiSusumu Horiguchi2016-11-14T02:37:56Z2016-11-14T02:37:56Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/1888This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/18882016-11-14T02:37:56ZA Hidden Topic-Based Framework toward Building Applications with Short Web DocumentsThis paper introduces a hidden topic-based framework for processing short and sparse documents (e.g., search result snippets, product descriptions, book/movie summaries, and advertising messages) on the Web. The framework focuses on solving two main challenges posed by these kinds of documents: 1) data sparseness and 2) synonyms/homonyms. The former leads to the lack of shared words and contexts among documents while the latter are big linguistic obstacles in natural language processing (NLP) and information retrieval (IR). The underlying idea of the framework is that common hidden topics discovered from large external data sets (universal data sets), when included, can make short documents less sparse and more topic-oriented. Furthermore, hidden topics from universal data sets help handle unseen data better. The proposed framework can also be applied for different natural languages and data domains. We carefully evaluated the framework by carrying out two experiments for two important online applications (Web search result classification and matching/ranking for contextual advertising) with large-scale universal data sets and we achieved significant results.Xuan Hieu Phanhieupx@vnu.edu.vnCam Tu NguyenDieu Thu LeLe Minh NguyenSusumu HoriguchiQuang Thuy Hathuyhq@vnu.edu.vn