NTNU @ Social Event Detection Task (SED)
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This document summarizes the NTNU@MediaEval 2011 Social Event Detection (SED) task. It describes the proposed framework which includes query expansion, search, categorization, clustering, and refinement. It outlines two challenges involving detecting music venues in Rome and Barcelona, and events at venues in Barcelona. It discusses experiments comparing runs with different refinement and categorization approaches. Results showed tag metadata and entity names improved performance, and refinement improved completeness. Future work includes better use of entity names for refinement and general event clustering.
![NTNU@MediaEval 2011: Social Event Detection Task (SED) Massimiliano Ruocco, Heri Ramampiaro Data and Information Management Group Department Of Computer and Information Science Norwegian University of Science and Technology [email_address] MediaEval 2011 Workshop - Pisa](https://image.slidesharecdn.com/ruoccopresentationshort-110906151429-phpapp01/85/NTNU-Social-Event-Detection-Task-SED-1-320.jpg)
![MediaEval2011 – SED Task Outline ,[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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NTNU @ Social Event Detection Task (SED)
- 1. NTNU@MediaEval 2011: Social Event Detection Task (SED) Massimiliano Ruocco, Heri Ramampiaro Data and Information Management Group Department Of Computer and Information Science Norwegian University of Science and Technology [email_address] MediaEval 2011 Workshop - Pisa
- 3. MediaEval2011 – SED Task Framework - Workflow Query Expansion Search Refinement Categorization Categorization Dataset LastFM SparQL Endpoint DBPedia Clustered List Clustering Semantic Merge
- 4. Challenge 1
- 8. Challenge 2
- 17. Thanks for the attention Questions? http://www.idi.ntnu.no/~ruocco/
Editor's Notes
- For each venue name in each different language the related spatial location a query is built. The tetxual terms of the venue name compose a boolean query by using the OR operator. A spatial and textual search is performed by the search engine for each input query related to each venue. A threshold on the search engine integrated score for the results occurrencies is used to select the results. The categorization is performed by using the three textual metadata surrounding every picture. Output is a set of flickr pictures grouped by venues which has SOCCER as topic.
- Then the similarity between two clusters is based on the number of shared entity names.
- In both refinement step is not in the process Giving a look over all the measures we can see a benefit using the refinement step and even a benefit using entity names instead of pure most frequent tags to refine the clusters. (observe the interesting increase of the recall measure) I would even remarks some point. Clustering evaluation is not an easy task since there are different metrics and different constraint can be verified. In particular two constraint that an evaluation metrics shoould evaluate are COMPLETENESS () and HOMOGEINITY (). For the first, Recall is a good indicator ad for the second recall and NMI are good indicator. With NMI it seems even to have increasing performarmce in term of completeness from the refinement by using top-100 tags and entity names
- In both refinement step is not in the process Giving a look over all the measures we can see a benefit using the refinement step and even a benefit using entity names instead of pure most frequent tags to refine the clusters. (observe the interesting increase of the recall measure) I would even remarks some point. Clustering evaluation is not an easy task since there are different metrics and different constraint can be verified. In particular two constraint that an evaluation metrics shoould evaluate are COMPLETENESS () and HOMOGEINITY (). For the first, Recall is a good indicator ad for the second recall and NMI are good indicator. With NMI it seems even to have increasing performarmce in term of completeness from the refinement by using top-100 tags and entity names
- In both refinement step is not in the process Giving a look over all the measures we can see a benefit using the refinement step and even a benefit using entity names instead of pure most frequent tags to refine the clusters. (observe the interesting increase of the recall measure) I would even remarks some point. Clustering evaluation is not an easy task since there are different metrics and different constraint can be verified. In particular two constraint that an evaluation metrics shoould evaluate are COMPLETENESS () and HOMOGEINITY (). For the first, Recall is a good indicator ad for the second recall and NMI are good indicator. With NMI it seems even to have increasing performarmce in term of completeness from the refinement by using top-100 tags and entity names