%0 Conference Paper %1 carpenter2007lingpipe %A Carpenter, B. %B Proceedings of the Second BioCreative Challenge Evaluation Workshop; 23 to 25 April 2007; Madrid, Spain %D 2007 %K language, lingpipe, models %P 307--309 %T LingPipe for 99.99\% recall of gene mentions %X User-contributed Web data contains rich and diverse infor- mation about a variety of events in the physical world, such as shows, festivals, conferences and more. This information ranges from known event features (e.g., title, time, location) posted on event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events) to discussions and reactions related to events shared on dierent social media sites (e.g., Twitter, YouTube, Flickr). In this paper, we focus on the challenge of automatically identifying user-contributed con- tent for events that are planned and, therefore, known in advance, across dierent social media sites. We mine event aggregation platforms to extract event features, which are often noisy or missing. We use these features to develop query formulation strategies for retrieving content associ- ated with an event on dierent social media sites. Further, we explore ways in which event content identied on one social media site can be used to retrieve additional relevant event content on other social media sites. We apply our strategies to a large set of user-contributed events, and an- alyze their eectiveness in retrieving relevant event content from Twitter, YouTube, and Flickr.

@inproceedings{carpenter2007lingpipe, abstract = {User-contributed Web data contains rich and diverse infor- mation about a variety of events in the physical world, such as shows, festivals, conferences and more. This information ranges from known event features (e.g., title, time, location) posted on event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events) to discussions and reactions related to events shared on dierent social media sites (e.g., Twitter, YouTube, Flickr). In this paper, we focus on the challenge of automatically identifying user-contributed con- tent for events that are planned and, therefore, known in advance, across dierent social media sites. We mine event aggregation platforms to extract event features, which are often noisy or missing. We use these features to develop query formulation strategies for retrieving content associ- ated with an event on dierent social media sites. Further, we explore ways in which event content identied on one social media site can be used to retrieve additional relevant event content on other social media sites. We apply our strategies to a large set of user-contributed events, and an- alyze their eectiveness in retrieving relevant event content from Twitter, YouTube, and Flickr.}, added-at = {2012-01-26T14:37:39.000+0100}, author = {Carpenter, B.}, biburl = {https://www.bibsonomy.org/bibtex/2d2c35c0818cd2d1da6a3b3432078b25a/edaehn}, booktitle = {Proceedings of the Second BioCreative Challenge Evaluation Workshop; 23 to 25 April 2007; Madrid, Spain}, interhash = {b802f0e394950b4be63e13cf4ccf4137}, intrahash = {d2c35c0818cd2d1da6a3b3432078b25a}, keywords = {language, lingpipe, models}, pages = {307--309}, timestamp = {2012-01-26T14:37:39.000+0100}, title = {LingPipe for 99.99\% recall of gene mentions}, year = 2007 }