Improving the performance of personal name disambiguation using web directories

Slide 1:Improving the performance of personal name disambiguation using web directories

Quang Minh Vu, Atsuhiro Takasu, Jun Adachi IPM, 2008 Presented by Hung-Yi Cai 2010/09/01

Slide 2:Outlines

Motivation Objectives Methodology Experiments Conclusions Comments

Slide 3:Motivation

Searching for information about a person on the internet is an increasing requirement in information retrieval. Search results returned from search engines for a personal name query often contain documents relevant to several people because a name is usually shared by several people. Due to this name ambiguity problem, users have to manually investigate the result documents to ?lter out people in whom they have no interest. 3

Slide 4:Previous Studies

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Slide 5:Objectives

Propose Similarity via Knowledge Base (SKB) that uses web directories to improve the disambiguating performance in Name Disambiguation System (NDS). SKB can be divided into two components: Using web directories as a knowledge base to ?nd common contexts by TF-IDF in documents. Then, using the common contexts measure to determine document similarities. 5

Slide 6:TF-IDF

6 Term weights are calculated using the terms� occurrences in the document concerned and in a set of documents. Tf (t, doc) is the number of times term t appears in the document doc.

Slide 7:Methodology

In SKB, using web directories to measures features of terms in a document. Measurement of term weights using a knowledge base A knowledge base Modification of term weight in documents Modification of term weight in directories Measurement of document similarities Find directories close in topic with the document Measure document similarities 7

Slide 8:Name Disambiguation System

The operational details are as follows: Preprocessing documents Calculation of document similarities Discrimination by reranking documents 8

Slide 9:Experiments

Step 1. Data Sets Documents of people Creation of pseudo namesake document sets and real namesake document sets 9

Slide 10:Experiments

Step 2. Web directory structures 10

Slide 11:Experiments

Step 3. Baseline methods Comparing SKB with two conventional methods: VSM: Calculating the weight of these terms by TF-IDF Building the feature vectors of documents NER: Extracting the entity names in the documents by LingPipe software Using these names to construct feature vectors of the documents (the constituents of vectors were binary values) 11

Slide 12:Experiments

Step 4. Evaluation metrics We recorded the precision values at 11 recall points: 0%, 10%, ... ,90%, and 100% and denoted these as P(doci, 0%), P(doci, 10%), ... , P(doci, 90%) and P(doci, 100%), respectively. 12

Slide 13:Step 5. Experimental results The overall performance for each method In this experiment, we set the window size n = 50 and the number of representative directories k = 20. We set the frequency document ratio threshold for SKB2 r = 5.

Experiments 13

Slide 14:Experiments

Step 5. Experimental results Performance of SKB2 when varying the frequency ratio threshold 14

Slide 15:Experiments

Step 5. Experimental results Performance of SKB systems when varying the window size 15

Slide 16:Experiments

Step 5. Experimental results Performance of SKBs when varying the number of representative directories 16

Slide 17:Experiments

Step 5. Experimental results Performance for each method on real namesake document sets 17

Slide 18:Conclusions

Disambiguation of people will be a trend in web search, and we propose a new method that uses web directories as a knowledge base to improve the disambiguation performance. The experimental results showed a significant improvement with our system over the other methods, and we also veri?ed the robustness of our methods experimentally with di?erent web directory structures and with di?erent parameter values.

Slide 19:Comments

Advantages Just requiring little preparation Broad range of people Shortages Cost of computation is proportional Some mistake Applications Information retrieval