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News Contextualization with Geographic and Visual Information

ACM Multimedia 2011. Zechao Li 1,2 , Meng Wang 3 , Jing Liu 1 , Changsheng Xu 1,2 and Hanqing Lu 1,2 1 Institute of Automation, Chinese Academy of Sciences 2 China-Singapore Institute of Digital Media 3 School of Computing National University of Singapore 29/11/2011.

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News Contextualization with Geographic and Visual Information

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  1. ACM Multimedia 2011 Zechao Li1,2, Meng Wang3, Jing Liu1, Changsheng Xu1,2 and Hanqing Lu1,2 1Institute of Automation, Chinese Academy of Sciences 2China-Singapore Institute of Digital Media 3School of Computing National University of Singapore 29/11/2011 News Contextualization with Geographic and Visual Information National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences

  2. Outline • Motivation • News Contextualization • Location relevance analysis • Image enrichment • Evaluation • Discussion

  3. Online News Reading – Popular News sites People: online reading

  4. Difficulties

  5. Online News Reading • Specific places • Hometown, working place, country • Incomprehensive visual information • Too few images

  6. Our Solution Semi-Supervised Learning Multimodal Fusion News Contextualization with Geographic and Visual Information News Contextualization Features/Distribution/Boosting Post-Processing

  7. Our Solution

  8. UI

  9. Location Relevance Analysis News from web Wikipedia1 GeoNames2 Toponym Candidates Toponym Filtering & Expansion 1http://en.wikipedia.org/wiki/Main_Page 2http://www.geonames.org/ Location Relevance Analysis

  10. Matrix Factorization Relevance Analysis • Basic idea • Where and What (news document) • Low rank • Factor model • Matrix factorization • Document similarity and toponym co-occurrence • Correlation Consistent Probabilistic Matrix Factorization (CCPMF) What Where Event Similarity Location Co-occurrence

  11. CCPMF • Notation • Rij: the initial relation between locations and documents • I: is the indicator matrix • P: the latent location feature matrix • E: the latent document feature matrix • LC and LS: the Laplacian matrices of document graph and location graph

  12. Image Enrichment Query Generation News Document Google Image Online Image Search Image Output

  13. Query Generation Difficulties Our Solution

  14. Queries Generation • Score terms in the title • Top c terms • L queries

  15. Queries Generation • An example {Obama bids China farewell with Great Wall tour}

  16. Image Mining & Selecting • How to find the appropriate images? • Score-based rank aggregation • Position & Visual similarity • Notation • h: the number of images in each list • k: the position of the i-th image in the j-th list • O: the set of original pictures

  17. Image Mining & Selecting • How to determine the weights? • Manually label some groundtruth • Tune the weights to maximize NDCG@15 • Top r images, including the original pictures

  18. Experiments • Data • ABC, BBC, CNN and Google News • 135,308 documents with 69,144 images • 4,742 locations • User Study • 30 persons, age 20-35 • two countries, frequently reading news online • NDCG • Very relevant, relevant, irrelevant: 2, 1, 0

  19. Experiment I – Location Relevance Analysis • News Search: NDCG • BM25 • PMF4 • Parameters 4 R. Salakhutdinov and A.Mnih. "Probabilistic Matrix Factorization". NIPS 2008

  20. Experiment II – Image Enrichment • Label 300 documents to train the weights • Compared method • Naïve Search: the whole title as a query • Naïve Fusion: each term in the title as a query

  21. Experiment III – NewsMap • Compared with Yahoo News Map • Convenience • Efficiency • Usefulness • Score: [1, 5]

  22. Conclusions • News browsing system: NewsMap • A novel matrix factorization to analyze the location relevance • Effective strategies to generate queries and intelligently fuse the results

  23. Future work • Organize news with a topic discovery component • News recommendation • Extent CCPMF to other potential applications such as shopping and several local services.

  24. Thank You

  25. Backup Slides

  26. News Ranking • Relevance, Timeliness and Importance • relevance: CCPMF • timeliness: ‘YYYYMMDD’ • importance: news similarity • PageRank

  27. Experiment-News Ranking • PRT: only time information • PRR: only the relevance • BM25

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