Intelligent Database Systems Lab Presenter: YU-TING LU Authors: Yong-Bin Kang, Pari Delir Haghighi, Frada Burstein 2014. ESA CFinder: An intelligent key.

Slides:

Advertisements

Similar presentations

Intelligent Database Systems Lab Presenter: WU, MIN-CONG Authors: Abdelghani Bellaachia and Mohammed Al-Dhelaan 2012, WIIAT NE-Rank: A Novel Graph-based.

Advertisements

Intelligent Database Systems Lab Presenter : YU-TING LU Authors : Harun Ug˘uz 2011.KBS A two-stage feature selection method for text categorization by.

Presented by Zeehasham Rasheed

Information retrieval Finding relevant data using irrelevant keys Example: database of photographic images sorted by number, date. DBMS: Well structured.

CSE 730 Information Retrieval of Biomedical Data The use of medical lexicon in biomedical IR.

Intelligent Database Systems Lab Presenter: YU-TING LU Authors: Laurens van der Maaten and Geoffrey Hinton ML Visualizing non-metric similarities.

Intelligent Database Systems Lab Presenter: HONG, CHIA-TSE Authors: Yen-Hsien Lee, Chih-Ping Wei, Tsang-Hsiang Cheng, Ching-Ting Yang DSS Nearest-neighbor-based.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Quality evaluation of product reviews using an information.

Intelligent Database Systems Lab Presenter: YU-TING LU Authors: Liang-Chu Chen, Ting-Jung Yu, Chia-Jung Hsieh ACM KeyGraph-based chance discovery.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Text classification based on multi-word with support vector.

Exploiting Wikipedia as External Knowledge for Document Clustering Sakyasingha Dasgupta, Pradeep Ghosh Data Mining and Exploration-Presentation School.

Leveraging Conceptual Lexicon ： Query Disambiguation using Proximity Information for Patent Retrieval Date : 2013/10/30 Author : Parvaz Mahdabi, Shima.

Learning Object Metadata Mining Masoud Makrehchi Supervisor: Prof. Mohamed Kamel.

Intelligent Database Systems Lab Presenter : CHANG, SHIH-JIE Authors : Shih-Hwa Liu*,Gwo-Guang Lee 2013.CE Using a concept map knowledge management system.

Using Text Mining and Natural Language Processing for Health Care Claims Processing Cihan ÜNAL

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Evaluation of novelty metrics for sentence-level novelty mining Presenter : Lin, Shu-Han Authors : Flora.

PAUL ALEXANDRU CHIRITA STEFANIA COSTACHE SIEGFRIED HANDSCHUH WOLFGANG NEJDL 1* L3S RESEARCH CENTER 2* NATIONAL UNIVERSITY OF IRELAND PROCEEDINGS OF THE.

Intelligent Database Systems Lab Presenter: WU, JHEN-WEI Authors: Rodrigo RizziStarr, Jose´ Maria Parente de Oliveira IS Concept maps as the first.

Intelligent Database Systems Lab Presenter : WU, MIN-CONG Authors : Jorge Villalon and Rafael A. Calvo 2011, EST Concept Maps as Cognitive Visualizations.

Text Mining In InQuery Vasant Kumar, Peter Richards August 25th, 1999.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Mining Positive and Negative Patterns for Relevance Feature.

Intelligent Database Systems Lab Presenter: WU, MIN-CONG Authors: Zhiyuan Liu, Wenyi Huang, Yabin Zheng and Maosong Sun 2010, ACM Automatic Keyphrase Extraction.

Intelligent Database Systems Lab Presenter: WU, MIN-CONG Authors: Zhiyuan Liu, Xinxiong Chen, Yabin Zheng, Maosong Sun 2011, FCCNLL Automatic Keyphrase.

Intelligent Database Systems Lab Presenter : JHOU, YU-LIANG Authors :Shady Shehata, Fakhri Karray, Mohamed S. Kamel, Fellow 2012, IEEE An Efficient Concept-Based.

Intelligent Database Systems Lab Presenter : YAN-SHOU SIE Authors Mohamed Ali Hadj Taieb *, Mohamed Ben Aouicha, Abdelmajid Ben Hamadou KBS Computing.

A progressive sentence selection strategy for document summarization Presenter : Bo-Sheng Wang Authors: You Quyang, Wenjie Li, Renxian Zhang, Qin Lu IPM,

Intelligent Database Systems Lab Presenter : JIAN-REN CHEN Authors : Sheng-Tun Li a,b,*, Fu-Ching Tsai a 2013, KBS A fuzzy conceptualization model for.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. A quantitative stock prediction system based on financial news Presenter : Chun-Jung Shih Authors :Robert.

Cube Kohonen Self-Organizing Map (CKSOM) Model

Intelligent Database Systems Lab N.Y.U.S.T. I. M. An IPC-based vector space model for patent retrieval Presenter: Jun-Yi Wu Authors: Yen-Liang Chen, Yu-Ting.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. An information-pattern-based approach to novelty detection Presenter : Lin, Shu-Han Authors : Xiaoyan.

Intelligent Database Systems Lab Presenter : Chang,Chun-Chih Authors : Youngjoong Ko, Jungyun Seo 2009, IPM Text classiﬁcation from unlabeled documents.

Intelligent Database Systems Lab Presenter : Kung, Chien-Hao Authors : Medhdi Khashei, Mehdi Bijari 2011, ASOC A novel hybridization of artificial neural.

Intelligent Database Systems Lab Presenter : Chang,Chun-Chih Authors : CHRISTOS BOURAS, VASSILIS TSOGKAS 2012, KBS A clustering technique for news articles.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Using Text Mining and Natural Language Processing for.

Intelligent Database Systems Lab Presenter : Kung, Chien-Hao Authors : Eghbal G. Mansoori 2011,IEEE FRBC: A Fuzzy Rule-Based Clustering Algorithm.

Intelligent Database Systems Lab Presenter : BEI-YI JIANG Authors : HAI V. PHAM, ERIC W. COOPER, THANG CAO, KATSUARI KAMEI INFORMATION SCIENCES Hybrid.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology Advisor ： Dr. Hsu Presenter ： Yu Cheng Chen Author: YU-SHENG.

Intelligent Database Systems Lab Presenter: CHANG, SHIH-JIE Authors: Kevin Meijer, Flavius Frasincar, Frederik Hogenboom 2014.DSS. A semantic approach.

Ranking Definitions with Supervised Learning Methods J.Xu, Y.Cao, H.Li and M.Zhao WWW 2005 Presenter: Baoning Wu.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Psychiatric document retrieval using a discourse-aware model Presenter : Wu, Jia-Hao Authors : Liang-Chih.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Improving the performance of personal name disambiguation.

Intelligent Database Systems Lab Presenter : JIAN-REN CHEN Authors : Wen Zhang, Taketoshi Yoshida, Xijin Tang 2011.ESWA A comparative study of TF*IDF,

Intelligent Database Systems Lab Presenter: CHANG, SHIH-JIE Authors: Longzhuang Li, Yi Shang, Wei Zhang 2002.ACM. Improvement of HITS-based Algorithms.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 1 Mining knowledge from natural language texts using fuzzy associated concept mapping Presenter : Wu,

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 1 Identifying Domain Expertise of Developers from Source Code Presenter : Wu, Jia-Hao Authors : Renuka.

Intelligent Database Systems Lab Presenter : Chuang, Kai-Ting Authors : Rafael Odon de Alencar, Clodoveu Augusto Davis Jr., Marcos André Gonçalves 2010,

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Automatic Document Indexing in Large Medical Collections.

Intelligent Database Systems Lab Presenter: YU-TING LU Authors: Christopher C. Yang and Tobun Dorbin Ng TSMCA Analyzing and Visualizing Web Opinion.

1 Question Answering and Logistics. 2 Class Logistics  Comments on proposals will be returned next week and may be available as early as Monday  Look.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Community self-Organizing Map and its Application to Data Extraction Presenter: Chun-Ping Wu Authors:

Intelligent Database Systems Lab Presenter: CHANG, SHIH-JIE Authors: Tao Liu, Zheng Chen, Benyu Zhang, Wei-ying Ma, Gongyi Wu 2004.ICDM. Improving Text.

Intelligent Database Systems Lab Presenter : JHOU, YU-LIANG Authors : Jae Hwa Lee, Aviv Segev 2012 CE Knowledge maps for e-learning.

Intelligent Database Systems Lab Presenter: YU-TING LU Authors: Junping Zhang, Hua Huang and Jue Wang IEEE INTELLIGENT SYSTEMS Manifold Learning.

Intelligent Database Systems Lab Presenter: YU-TING LU Authors: Vittorio Carlei, Massimiliano Nuccio PRL Mapping industrial patterns in spatial agglomeration:

Intelligent Database Systems Lab Presenter : Fen-Rou Ciou Authors : Hamdy K. Elminir, Yosry A. Azzam, Farag I. Younes 2007,ENERGY Prediction of hourly.

Intelligent Database Systems Lab Presenter : Chang,Chun-Chih Authors : Emilio Corchado, Bruno Baruque 2012 NeurCom WeVoS-ViSOM: An ensemble summarization.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Visualizing social network concepts Presenter : Chun-Ping Wu Authors :Bin Zhu, Stephanie Watts, Hsinchun.

Intelligent Database Systems Lab Presenter : YU-TING LU Authors : Hsin-Chang Yang, Han-Wei Hsiao, Chung-Hong Lee IPM Multilingual document mining.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Named Entity Disambiguation by Leveraging Wikipedia Semantic Knowledge Presenter : Jiang-Shan Wang Authors.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Enhancing Text Clustering by Leveraging Wikipedia Semantics.

An Ontology-based Automatic Semantic Annotation Approach for Patent Document Retrieval in Product Innovation Design Feng Wang, Lanfen Lin, Zhou Yang College.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. A method of extracting malicious expressions in bulletin board systems by using context analysis Presenter:

Intelligent Database Systems Lab Presenter : BEI-YI JIANG Authors : JAMAL A. NASIR, IRAKLIS VARLAMIS, ASIM KARIM, GEORGE TSATSARONIS KNOWLEDGE-BASED.

Ricardo EIto Brun Strasbourg, 5 Nov 2015

JPEG Compressed Image Retrieval via Statistical Features

Arabic Text Categorization Based on Arabic Wikipedia

Using lexical chains for keyword extraction

Development of the Amphibian Anatomical Ontology

CSE 635 Multimedia Information Retrieval

Presentation transcript:

Intelligent Database Systems Lab Presenter: YU-TING LU Authors: Yong-Bin Kang, Pari Delir Haghighi, Frada Burstein ESA CFinder: An intelligent key concept finder from text for ontology development

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

Intelligent Database Systems Lab Motivation According to prior studies, concepts can be often described by noun phrases that are suitable for representing the key information within text documents. The main problem under this scheme is that not all the noun phrases can be considered as domain- specific concepts and useful for accurately conceptualizing domain knowledge.

Intelligent Database Systems Lab Objectives We propose a novel key concept finder named CFinder that combines NLP techniques, statistical knowledge and domain-specific knowledge from a corpus of documents in the target domain. The calculated weights are further enhanced by considering an inner structural pattern of the candidates.

Intelligent Database Systems Lab Methodology

Intelligent Database Systems Lab Methodology-Key concept candidate extraction Noun phrase extraction using POS tags Synonym finding and stopword removing Candidate enrichment EMS  Emergency Medical Service

Intelligent Database Systems Lab Methodology - Weight calculation of key concept candidates Statistical knowledge Domain- specific knowledge

Intelligent Database Systems Lab Methodology-Key concept extraction c: candidate(key phrase) D: corpus d: document

Intelligent Database Systems Lab Experiments - The DO4MG ontology

Intelligent Database Systems Lab Experiments - Evaluation metrics

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab Conclusions CFinder has a strong ability to improve the effectiveness of key concept extraction. CFinder can be built and combined to estimate their degrees of relevance in the target domain, and how the inner structural patterns of the candidates were further enhanced to identify key concepts within the knowledge sources.

Intelligent Database Systems Lab Comments Advantages - Improve the effectiveness of key concept extraction. - It’s not restricted to any domains or applications Applications - Text categorization - Text summarization - Information retrieval