Web Search Clustering and Labeling with Hidden Topics

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

Web Search Clustering and Labeling withHidden Topics

Presenter ： Chien-Hsing ChenAuthor: Cam-Tu Nguyen Xuan-Hieu Phan Susumu Horiguchi Thu-Trang Nguyen Quang-Thuy Ha

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2009.TALIP.40.


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Outline Motivation Objective Method Experiments Conclusion Comment


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d1: ezPeer+ 音樂下載、音樂試聽、歌詞、 MP3 、音樂網 - 蔡依林 - 歷年專輯 ezPeer+ – 蔡依林 - J1 Live Concert 演唱會影音全紀錄 ,J-game, 看我 72 變 , 城堡 ,J9 Party 派對精選 ,Jolin J- Top 冠軍精選 , 舞孃 , 蔡依林唯舞獨尊演唱會鮮聽版 & 混音專輯 & 花 ... web.ezpeer.com/singer/s120.html - 頁庫存檔 - 類似內容

d2: ezPeer+ 音樂下載、音樂試花蝴蝶好聽… web.ezpeer.com/singer/s120.html - 頁庫存檔 - 類似內容

The snippets are usually noisier, less topic-focused, and much shorter 花 ??

similarity evaluation between snippets may not be successful

Motivation

d3: {He is an author}d4: {The writer is standing behind you}


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Similarity evaluator is referred to a set of hidden topics

di: {He is an author}dj: {The writer is standing behind you}

(a document may be related to multi-topics)

Objective


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Framework

(label candidate generation)

di > topic10dj > topic10

djdi

musicmovieradio player

musicmovie


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LDA

music movie author kill writer book …

1 1 0 0 1 1 …

1 1 0 0 1 1 …

k topicm documentn word

zm,n

wm,n

k = 10 (show business)K=60

z1

w1

1 1 0 0 1 1 …

1 0 0 0 0 0 …

z2

w2

1 1 0 0 0 0 …

1 0 0 0 0 0 …

z3

w3

politicsentertainment

show business

edu. cul.hel.

the word “music” in the topic 10 can explain the occurrence of the words in the documents m=1,2,3

In training step:the keyword is related to a topic when it often occurs in the documents topic

refer to topic k

refer to vocabulary


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LDA

music movie author move writer book …

1 1 0 0 1 1 …

1 1 0 1 1 1 …

k topicm documentn word zm,n

wm,nk = topic 10K=60

z1

w1


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LDA


1 1 0 0 1 1 …

1 1 0 1 1 1 …


zm,n


z1

w1

1 2 3 4 … 9 10 11 11 … 60

dm0.2 0.1 0.4 0.3 … 0.2 0.9 0.1 0.2 … 0.1

p(.|.)=?


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LDA


1 1 0 0 1 1 …

1 1 0 1 1 1 …


zm,n


z1

w1

1 2 3 4 … 9 10 11 11 … 60

dm0.2 0.1 0.4 0.3 … 0.2 0.9 0.1 0.2 … 0.1

p(.|.)=?

p(.|.)=1/60


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Framework


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the tth term in the vocabulary V

the kth topic

Similarity between di and dj


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Framework

similarity matrix between snippets


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Label Candidate Generation

D Topic k

k=1 k=2 … k=10 … k=60

music 14 18 38 9Label Candidate Generation

music

radio player

mp3

CD

…


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Label assignment for clustering snippets

D Topic k

Label Candidate Generation

music

radio player

mp3

CD

…

dj

di

Label assignment

music

CD


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Framework

(label candidate generation)

di > topic10dj > topic4, topic10

djdi

musicmovieradio layer

musicmovie


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Experiment

Wikipedia datasetVnexpress dataset


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Experimental dataset

Web dataset consists of 2,357 snippets in 9 categories

20 queries to Google and obtaining about 150 distinguished snippets


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F-measure

Experiments


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Experiments


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Experiments


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Experiments


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Experiments


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Experiments


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Experiments


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clustering snippets with hidden topics labeling clusters using hidden topic analysis

Conclusion


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Advantage labeling clusters with the help of hidden topics the size of snippets is small

Two datasets: 2,357 and 150 (in our work: more than 2 million snippets)

Disadvantage less depends on snippets

Application snippets are useful to make sense

My Comment

Documents

Web Search Clustering and Labeling with Hidden Topics