Improving the performance of personal name disambiguation using web directories

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

2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

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 filter out people in whom they have no interest.

4. Previous Studies

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 find common contexts by TF-IDF in documents. • Then, using the common contexts measure to determine document similarities.

6. TF-IDF • 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.

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

8. Name Disambiguation System • The operational details are as follows: • Preprocessing documents • Calculation of document similarities • Discrimination by reranking documents

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

10. Experiments • Step 2. Web directory structures

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)

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.

13. Experiments • 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.

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

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

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

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

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 verified the robustness of our methods experimentally with different web directory structures and with different parameter values.

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