1. Link Distribution on Wikipedia [0407]KwangHee Park

2. Table of contents  Introduction  Topic modeling  Preliminary Problem  Conclusion

3. Introduction  Why focused on Link  When someone make new article in Wikipedia, mostly they simply link to other language source or link to similar and related article. After that, that article to be wrote by others  Assumption  Link terms in the Wikipedia articles is the key terms which can represent specific characteristic of articles

4. Introduction  Problem what we want to solve is  To analyses latent distribution of set of Target document by topic modeling

5. Topic modeling  Topic  Topics are latent concepts buried in the textual artifacts of a community described by a collection of many terms that co-occur frequently in context Laura Dietz, Avaré Stewart 2006 ‘ Utilize Probabilistic Topic Models to Enrich Knowledge Bases’  T = {W i,…,W n }

6. Topic modeling  Bag of word assumption  The bag-of-words model is a simplifying assumption used in natural language processing and information retrieval. In this model, a text (such as a sentence or a document) is represented as an unordered collection of words, disregarding grammar and even word order. From Wikipedia  Each document in the corpus is represented by a vector of integer  {f 1,f 2,…f |w| }  F i = frequency of i th word  |w| = number of words

7. Topic modeling  Instead of directly associating documents to words, associate each document with some topics and each topic with some significant words  Document = {T n, T k,…,T m }  {Doc : 1 }  {T n : 0.4, T k : 0.3,… }

8. Topic modeling  Based upon the idea that documents are mixtures of topics  Modeling  Document  topic  term

9. Topic modeling  LSA  performs dimensionality reduction using the singular value decomposition.  The transformed word– document co-occurrence matrix, X, is factorized into three smaller matrices, U, D, and V.  U provides an orthonormal basis for a spatial representation of words  D weights those dimensions  V provides an orthonormal basis for a spatial representation of documents.

10. Topic modeling  pLSA Observed word distributions word distributions per topic Topic distributions per document

11. Topic modeling  LDA (Latent Dirichlet Allocation)  Number of parameters to be estimated in pLSA grows with size of training set  In this point LDA method has advantage  Alpha and beta are corpus-level documents that are sampled once in the corpus creating generative model (outside of the plates!) pLSA LDA

12. Topic modeling – our approach  Target  Document = Wikipedia article  Terms = linked term in document  Modeling method  LDA  Modeling tool  Lingpipe api

13. Advantage of linked term  Don’t need to extra preprocessing  Boundary detection  Remove stopword  Word stemming  Include more semantics  Co-relation between term and document  Ex) cancer as a term  cancer as a document cancer A Cancer

14. Preliminary Problem  How well link terms in the document are represent specific characteristic of that document  Link evaluation  Calculate similarity between document

15. Link evaluation  Similarity based evaluation  Calculate similarity between terms  Sim_t{term1,term2}  Calculate similarity between documents  Sim_d{doc1,doc2}  Compare two similarity

16. Link evaluation  Sim_t  Similarity between terms  Not affected input term set  Sim_d  Similarity between documents  Significantly affected input term set p,q = topic distribution of each document Lin 1991

17. Link evaluation  Compare top 10 most similar each link  Ex )Link A  Term list most similar to A as term  Document list most similar to A as document  Compare two list – number of overlaps  Now under experiment

18. Conclusion  Topic modeling with link distribution in Wikipedia  Need to measure how well link distribution can represent each article’s characteristic  After that analysis topic distribution in variety way  Expect topic distribution can be apply many application

19. Thank