Result Diversity and Entity Ranking Experiments: Anchors, Links, Text and Wikipedia

Rianne Kaptein, Marijn Koolen, Jaap Kamps
2009 Text Retrieval Conference  
In this paper, we document our efforts in participating to the TREC 2009 Entity Ranking and Web Tracks. We had multiple aims: For the Web Track's Adhoc task we experiment with document text and anchor text representation, and the use of the link structure. For the Web Track's Diversity task we experiment with using a top down sliding window that, given the top ranked documents, chooses as the next ranked document the one that has the most unique terms or links. We test our sliding window method
more » ... on a standard document text index and an index of propagated anchor texts. We also experiment with extreme query expansions by taking the top n results of the initial ranking as multi-faceted aspects of the topic to construct n relevance models to obtain n sets of results. A final diverse set of results is obtained by merging the n results lists. For the Entity Ranking Track, we also explore the effectiveness of the anchor text representation, look at the co-citation graph, and experiment with using Wikipedia as a pivot. Our main findings can be summarized as follows: Anchor text is very effective for diversity. It gives high early precision and the results cover more relevant sub-topics than the document text index. Our baseline runs have low diversity, which limits the possible impact of the sliding window approach. New link information seems more effective for diversifying text-based search results than the amount of unique terms added by a document. In the entity ranking task, anchor text finds few primary pages , but it does retrieve a large number of relevant pages. Using Wikipedia as a pivot results in large gains of P10 and NDCG when only primary pages are considered. Although the links between the Wikipedia entities and pages in the Clueweb collection are sparse, the precision of the existing links is very high.
dblp:conf/trec/KapteinKK09 fatcat:viwwrzxdujfh5cznzq5vqrw6de