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Exploiting Wikipedia as External Knowledge for Document Clustering

Xiaohua Hu, Xiaodan Zhang, Caimei Lu, E. K. Park, and Xiaohua Zhou

Proceeding of International Conference on Knowledge Discovery and Data Mining, ACM SIGKDD, 2009

報告人 : 吳建良

Outline Motivation Framework of Wikipedia-based clustering

Concept mapping schemes Category mapping Document clustering

Experiments Conclusions

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Motivation Traditional text clustering algorithm

Based on BOW (Bag of Word) Ignore the semantic relationship among words

Synonym or semantically associated in other forms

One way to resolve this problem Use background knowledge to enrich document

representation Background knowledge is described by an ontology Ontology: concepts, attributes, relationships

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Motivation (cont.)

Problem of this approach based on an ontology Difficult to find a comprehensive ontology to cover

all the concepts Previous works has adopted WordNet and Mesh

Replace original content with ontology term Information loss

Add ontology term to original document vector Bring data noise into the dataset

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Goal Adopt more comprehensive ontology

Wikipedia Fully leverage ontology terms and relations

without introducing more noise Two matching methods

Exact-match Relatedness-match

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Framework

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Mapping Document to Wikipedia Concepts and Categories

Mapping process includes three steps:1. Build the connection between Wikipedia concepts

and categories

2. Map each document into a vector of Wikipedia concepts

3. Match each document to a set of Wikipedia categories

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Figure of three steps

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Step1

Step2

Step3

Concept-Category Matrix In Wikipedia, each topic is described by only one

article Title of the article preferred concept Each article (concept) has the corresponding categories Example:

Concept: Cluster Analysis Categories: Data mining | Data analysis | Cluster analysis |

Geostatistics | Machine learning | Multivariate statistics | Knowledge discovery in databases

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Document-Concept Matrix Built matrix through two matching schemes

Exact-match Relatedness-match

Exact-match Issue: how to map synonymous phrases to the same concept Use redirect links in Wikipedia Example:

Preferred concept: cluster analysis Redirected concepts: data clustering,… are redirected to the same article

Use preferred and redirected concepts to construct a dictionary10

Exact-Match Scheme Each document is scanned to find concepts of dictionary Only preferred concepts are used to build the concept

vector for each document

Based on this frequency matrix Further calculate the document-concept TFIDF matrix

Efficient, but has low recall Product good results only when Wiki has good coverage

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Preferred_concept1 Preferred_concept2

Doc1 Freq_pre_con1 + Freq_all_redi_con1 Freq_pre_con2 + Freq_all_redi_con2

Relatedness-Match Scheme Consist of two steps

1. First, create Wikipedia term-concept matrix from Wikipedia article collection

Each word token is represented by a concept vector Values of the vector are TFIDF scores For each word, only choose top k=5 concepts with highest TFIDF

scores

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Relatedness-Match Scheme (cont.)

2. Use word-concept matrix as a bridge to associate documents with Wikipedia concepts

Calculate relatedness of a Wikipedia concept to a given document

: a document collection : all Wikipedia preferred concepts For each document, select top M=200 concepts with highest relatedness

score Concept relatedness score vector is normalized Especially useful when Wikipedia concepts have less coverage for a dataset

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Category Mapping for Exact-Match Document-category frequency matrix

Derived from document-concept frequency matrix Replace each concept with its corresponding categories Calculate frequency of a category:

Further derive the document-category TFIDF matrix14

CAT1 CAT2

C1 1 0

C2 1 1

C1 C2

D1 9 2

D2 3 5

}CAT1 CAT2

D1 9+2 2

D2 3+5 5

Category Mapping for Relatedness-Match

Document-category matrix Derived from document-concept relatedness matrix Replace each concept with its corresponding categories Calculate relatedness score of a category:

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CAT1 CAT2

C1 1 0

C2 1 1

C1 C2

D1 0.3 0.7

D2 0.63 0.37

}CAT1 CAT2

D1 0.3+0.7 0.7

D2 0.63+0.37 0.37

Document Clustering Agglomerative clustering algorithm

1. Initially, each document starts as a cluster

2. Repeatedly merge closest pair of clusters

3. Until only one cluster is formed covering all documents

Similarity measure

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Closest Pair of Clusters Calculation

Single linkage

Complete linkage Adopted in this paper

Average linkage

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C1 C2

Partitional Clustering

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K-means clustering algorithm

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Arbitrarily choose K object as initial cluster center

Assign each objects to most similar center

Update the cluster means

Update the cluster means

reassignreassign

Partitional Clustering (cont.)

Similarity measure

Clustering result is influenced by initial selection of cluster centroids Evaluation:

Run ten times with random initialization Take average as the final clustering result

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Experiments Wikipedia data

Download from http://download.wikipedia.org 911,028 articles and 29,000 categories

Clustering dataset TDT2: 7,094 documents, 10 classes LA Times (from TREC): 18,547 documents from top

ten sections, 10 classes 20-newgroups (20NG): 19,997 documents, 20 classes

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Experiments (cont.)

For each dataset, five small datasets are created Method:

For each small dataset, randomly pick 100 documents from each selected class of a given dataset

Merge them into a big pool Cluster each small dataset separately Average result is viewed as the clustering result for

whole dataset

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Evaluation Metrics Purity

Average percentage of the dominant class label in each cluster

F-score Combine precision and recall to compute score

Normalized mutual information (NMI)

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%1001

K

C

C

purity

K

ii

di

recallprecision

recallprecisionF

)(2

2/)log(log

);(),(

ck

YXIYXNMI

Agglomerative Clustering Results

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Summary of this result Word_Category performs better than Word_Concept_Category Combining Word and Category significantly improve clustering

result Category information is more useful than concept information

Word_Concept improves clustering result, but not significant Clustering only based on Concept performs worse than the

baseline Still contain too much noise

Do not disambiguate concept senses during concept mapping process

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Partitional Clustering Results

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Summary of this result For 20 Newsgroup, Word_Category scheme still significantly

improve clustering result F-Score and Purity of Word_Concept_Category based clustering

are significantly improved For 20 Newsgroup , RM always produces better result than EM For LATimes and TDT2, EM always outperforms RM

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Conclusion A framework

Leverage Wikipedia concept and category information to improve text clustering performance

Mapping Schemes Exact-Match and Relatedness-Match

Concept vector and Category vector Two clustering approaches on three datasets

Agglomerative and partitional clustering

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