Intelligent Database Systems Lab Presenter: WU, MIN-CONG Authors: Yongzheng Zhang, Rajyashree...

Intelligent Database Systems Lab

Presenter: WU, MIN-CONG

Authors: Yongzheng Zhang , Rajyashree Mukherjee ,

Benny Soetarman

2012, ACM

Concept Extraction for Online Shopping


Outlines

MotivationObjectivesMethodologyExperimentsConclusionsComments

1


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


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


Methodology - ACE

ACE

ICE

KEA

5


Methodology - ACE

ACE

ICE

KEA

ACE

ICE

KEA

Trem frequency

6


Methodology - ACE

ACE

ICE

KEA

ACE

HTML Scorer

TF Scorer

ACE

ICE

KEA

Tokenization

Concept Miner

Concept Derivation

7


Methodology - ACE

ACE

ICE

KEA

ACE

HTML Scorer

ACE

ICE

KEA

Tokenization

Concept Miner

Concept Derivation

TF Scorer

8


Methodology - ACE

ACE

ICE

KEA

ACE

TF Scorer

ACE

ICE

KEA

Tokenization

Concept Miner

Concept Derivation

HTML Scorer

9


ACE

ICE

KEA

Methodology - ICE

ACE

ICE

ACE

ICE

KEA

10


ACE

ICE

KEA

Methodology - ICE

ACE

ICE

ACE

ICE

KEA

11


ACE

ICE

KEA

Methodology - ICE

ACE

ICE

ACE

ICE

KEA

<a href = “http://buy.ebay.com/cell-phone”>cell phone</a> <a href =“http://www.ebay.com/”>Home</a>

cell phone homecell

phonehome

12


ACE

ICE

KEA

Methodology - ICE

ACE

ICE

ACE

ICE

KEA

Baseball

Professional Baseball

Baseball Players

Professional Baseball Players

13


Baseball

Professional Baseball

Professional Baseball Players

ACE

ICE

KEA

Methodology - ICE

ACE

ICE

ACE

ICE

KEA

Baseball Players

Baseball Players

14


ACE

ICE

KEA

Methodology - ICE

ACE

ICE

ACE

ICE

KEA

Emphasis scorer

ACE

HTML Scorer

TF Scorer

Concept Miner

15


ACE

ICE

KEA

Methodology - ICE

ACE

ICE

ACE

ICE

KEA

ACE

HTML Scorer

TF Scorer

Concept Miner

15

overlapping title

Emphasis scorer


ACE

ICE

KEA

Methodology - ICE

ACE

ICE

ACE

ICE

KEA

Porter stemming

Baseball baseballs Baseballs

baseball

17


ACE

ICE

KEA

Methodology - KEA

ACE

ICE

KEA

ACE

Human authored

TF-IDF

first appearance

Naïve Bayes model

18


Experiment

11 Evaluation framework

ACE V.S ICE

T

B

λ

ICE V.S KEA

Document Topic

100 shopping related Web pages Dell, HP, and Canon

19


Experiment


ICE V.S KEA

Document Topic


ACE V.S ICE

T

B

λ

ACE V.S ICE[B,T]

20


Experiment


Document Topic


ACE V.S ICE

T

B

λ

ICE V.S KEA

KEA50 Web

pages for training

ICE

precision recall F1-measure

ICE 0.7383 0.8300 0.7815

KEA 0.6583 0.7600 0.7056

21


Conclusions• The experimental results demonstrate that ICE

significantly outperforms KEA in concept extraction

for online shopping.

22


Comments• Advantages– ICE is an unsupervised method that doesn’t need

to Human-authored keyphrase.• Applications– online shopping, concept extraction, automatic keyphrase extraction.

23

Intelligent Database Systems Lab Presenter: WU, MIN-CONG Authors: Yongzheng Zhang, Rajyashree...

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