The University of Amsterdam at TREC 2009: Blog, Web, Entity, and Relevance Feedback

We describe the participation of the University of Amsterdam's ILPS group in the web, blog, web, entity, and relevance feedback track at TREC 2009. Our main preliminary conclusions are as follows. For the Blog track we find that for top stories identification a blogs to news approach outperforms a simple news to blogs approach. This is interesting, as this approach starts with no input except for a date, whereas the news to blogs approach also has news headlines as input. For the web track, we find that spam is an important issue in the ad hoc task and that Wikipedia-based heuristic optimization approaches help to boost the retrieval performance, which is assumed to potentially reduce the spam in top ranked documents. As for the diversity task, we explored different methods. Initial results show that clustering and a topic model-based approach have similar performance, which are relatively better than a query log based approach. Our performance in the Entity track was downright disappointing; the use of co-occurrence models led to poor results; an initial analysis shows that while our approach is able to find correct entity names, we fail to find homepages for these entities. For the relevance feedback track we find that a topical diversity approach provides good feedback documents. Further, we find that our relevance feedback algorithm seems to help most when there are sufficient relevant documents available.