ENHANCING CLUSTER LABELING USING WIKIPEDIA

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ENHANCING CLUSTER LABELING USING WIKIPEDIA David Carmel, Haggai Roitman, Naama Zwerdling IBM Research Lab SIGIR’09
,Document Clustering A method of aggregating a set of documents such that :
Documents within cluster are as similar as possible.
Documents from different clusters should be dissimilar. Cluster 2 Cluster 1 Cluster 3
,Cluster Labeling To assign each cluster a human readable label that can best represent the cluster.
Traditional method is to pick the label from the important terms within the cluster. The statistically significant terms may not be a good label.
A good label may not occur directly in the text. Electronics Bowling Ice Hockey Cluster 2 Cluster 1 Cluster 3
,Approach Utilizing the external resource to help the cluster labeling.
Besides the important terms extracted from the cluster, the metadata of Wikipedia such as title and category is used to serve as candidate label. ,A General Framework i i i i i i
,Step1: Indexing Documents are parsed and tokenized.
Term weight are determined by tf-idf. Use Lucene to generate a search index such that the tf and idf value of term t can be quickly accessed. ,Step2: Clustering Given the document collection D, return a set of document clusters C={C1,C2,…,Cn}.
A cluster is represented by its centroid of the cluster’s documents. The term weight of the cluster’s centroid is slightly modified: ,Step3: Important Terms Extraction Given a cluster , find a list of important terms ordered by their estimated importance.
This can be achieved by Selecting the top weighted terms from the cluster centroid.
Use the Jensen-Shannon Divergence(JSD) to measure the distance between the cluster and the collection. ,Step4: Label Extraction One way is to use the top k important terms directly.
The other way is to use the top k important terms to query Wikipedia. The title and the set of categories of the returned Wiki documents serve as candidate labels. ,Step5: Output the Recommended Labels from Candidate Labels MI(Mutual Information) Judge
Score each candidate label by its pointwise mutual information with the cluster’s important terms.
SP(Score Propagation) Judge Propagate the document score to the candidate label.
Document score can be the original score of the IR system or the rank(d)-1
Socore Aggregation Use linear combination to combine the above two judges.
The recommend labels are the top ranked labels. ,Data Collection 20 News Groups
20 (clusters) X 1000 (documents/ clusters)
Open Directory Project(ODP) 100 (clusters) X 100 (documents/ clusters)
The Ground Truth The correct label itself.
The correct label’s inflection. The correct label’s Wordnet synonym . ,Evaluation Metrics label1 label1 label1 label1 Match@K
Ex:
Mean Reciprocal Rank(MRR@K) Ex:
label2 label2 label2 label2 label3 label3 label3 label3 Match@4
=1/2 =0.5 Correct Correct label4 label4 label4 label4 c1 c1 c2 c2 MRR@4 =((1/2)+(1/3))/2
=0.416… Correct
,Parameters The important term selection method(JSD, ctf-cdf-idf, MI, chi-square).
The number of important terms for querying Wikipedia. The number of Wikipedia results to be used for label extraction. The judges used for candidate evaluation. ,Evaluation 1 The effectiveness of using Wikipedia to enhance cluster labeling.
,Evaluation 2 Candidate label extraction
,Evaluation 3 Judge effectiveness
,Evaluation 4.1 The Effect of Clusters’ Coherency on Label Quality
Testing on a "noisy cluster": For a noise level p(in [0,1]) of clusters, each document in one cluster have probability p to swap with document in other cluster.
,Evaluation 4.2 The Effect of Clusters’ Coherency on Label Quality
,Conclusion Proposed a general framework for solving cluster labeling problem.
The metadata of Wikipedia can boost the performance of cluster labeling. The proposed method has good resiliency to noisy clusters.

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