VideoCLEF 2008: ASR Classification based on Wikipedia Categories.
Jens Kürsten, Daniel Richter, Maximilian Eibl · CLEF (Working Notes) · 2008
This article describes our participation at the VideoCLEF track of the CLEF campaign 2008. We designed and implemented a prototype for the classification of the Video ASR data. Our approach was to regard the task as text classification problem. We used terms from Wikipedia categories as training data for our text classifiers. For the text classification the Naive-Bayes and kNN classifier from the WEKA toolkit were used. We submitted experiments for classification task 1 and 2. For the translation of the feeds to English (translation task) Google’s AJAX language API was used. The evaluation of the classification task showed bad results for our experiments with a precision between 10 and 15 percent. These values did not meet our expectations. Interestingly, we could not improve the quality of the classification by using the provided metadata. But at least the created translation of the RSS Feeds was well.