Intelligent Database Systems Lab Presenter: WU, MIN-CONG Authors: Yongzheng Zhang, Rajyashree Mukherjee, Benny Soetarman 2012, ACM Concept Extraction for.

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Presentation transcript:

Intelligent Database Systems Lab Presenter: WU, MIN-CONG Authors: Yongzheng Zhang, Rajyashree Mukherjee, Benny Soetarman 2012, ACM Concept Extraction for Online Shopping

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments 1

Intelligent Database Systems Lab Motivation In order to provide a more streamlined user experience in shopping related research, it is critical for e-commerce sites to accurately identify what a Web page is talking about. 2

Intelligent Database Systems Lab Objectives We investigate two concept extraction methods ACE and KEA in the online shopping context. We discuss how to upgrade ACE with major improvements into ICE. 3

Intelligent Database Systems Lab Methodology - ACE ACE ICE KEA 5

Intelligent Database Systems Lab Methodology - ACE ACE ICE KEA ACE ICE KEA Trem frequency 6

Intelligent Database Systems Lab Methodology - ACE ACE ICE KEA ACE HTML Scorer TF Scorer ACE ICE KEA Tokenization Concept Miner Concept Derivation 7

Intelligent Database Systems Lab Methodology - ACE ACE ICE KEA ACE HTML Scorer ACE ICE KEA Tokenization Concept Miner Concept Derivation TF Scorer 8

Intelligent Database Systems Lab Methodology - ACE ACE ICE KEA ACE TF Scorer ACE ICE KEA Tokenization Concept Miner Concept Derivation HTML Scorer 9

Intelligent Database Systems Lab ACE ICE KEA Methodology - ICE ACE ICE ACE ICE KEA 10

Intelligent Database Systems Lab ACE ICE KEA Methodology - ICE ACE ICE ACE ICE KEA 11

Intelligent Database Systems Lab ACE ICE KEA Methodology - ICE ACE ICE ACE ICE KEA cell phone Home cell phone home cell phone home 12

Intelligent Database Systems Lab ACE ICE KEA Methodology - ICE ACE ICE ACE ICE KEA Baseball Professional Baseball Baseball Players Professional Baseball Players 13

Intelligent Database Systems Lab Baseball Professional Baseball Professional Baseball Players ACE ICE KEA Methodology - ICE ACE ICE ACE ICE KEA Baseball Players Baseball Players 14

Intelligent Database Systems Lab ACE ICE KEA Methodology - ICE ACE ICE ACE ICE KEA Emphasis scorer ACE HTML Scorer TF Scorer Concept Miner 15

Intelligent Database Systems Lab ACE ICE KEA Methodology - ICE ACE ICE ACE ICE KEA ACE HTML Scorer TF Scorer Concept Miner 15 overlapping title Emphasis scorer

Intelligent Database Systems Lab ACE ICE KEA Methodology - ICE ACE ICE ACE ICE KEA Porter stemming Baseball baseballs Baseballs baseball 17

Intelligent Database Systems Lab ACE ICE KEA Methodology - KEA ACE ICE KEA ACE Human authored TF-IDF ﬁrst appearance Naïve Bayes model 18

Intelligent Database Systems Lab Experiment 11 Evaluation framework ACE V.S ICE T T B B λ λ ICE V.S KEA DocumentTopic 100 shopping related Web pagesDell, HP, and Canon 19

Intelligent Database Systems Lab Experiment 11 Evaluation framework ICE V.S KEA DocumentTopic 100 shopping related Web pagesDell, HP, and Canon ACE V.S ICE T T B B λ λ [B,T] 20

Intelligent Database Systems Lab Experiment 11 Evaluation framework DocumentTopic 100 shopping related Web pagesDell, HP, and Canon ACE V.S ICE T T B B λ λ ICE V.S KEA KEA 50 Web pages for training ICE precisionrecallF1-measure ICE KEA

Intelligent Database Systems Lab Conclusions The experimental results demonstrate that ICE signiﬁcantly outperforms KEA in concept extraction for online shopping. 22

Intelligent Database Systems Lab Comments Advantages – ICE is an unsupervised method that doesn’t need to Human-authored keyphrase. Applications – online shopping, concept extraction, automatic keyphrase extraction. 23