Tuesday, July 13, 2010

Jaime Arguello -- Friday July 16th

Time: Noon
Location: TBA

Speaker: Jaime Arguello, Language Technologies Institute, School of Computer Science, CMU

Title: Vertical Selection in the Presence of Unlabeled Verticals.


Vertical aggregation is the task of incorporating results from specialized search engines or verticals (e.g., images, video, news) into Web search results. Vertical selection is the subtask of deciding, given a query, which verticals, if any, are relevant. State of the art approaches use machine learned models to predict which verticals are relevant to a query. When trained using a large set of labeled data, a machine learned vertical selection model outperforms baselines which require no training data. Unfortunately, whenever a new vertical is introduced, a costly new set of editorial data must be gathered. In this paper, we propose methods for reusing training data from a set of existing (source) verticals to learn a predictive model for a new (target) vertical. We study methods for learning robust, portable, and adaptive cross-vertical models. Experiments show the need to focus on different types of features when maximizing portability (the ability for a single model to make accurate predictions across multiple verticals) than when maximizing adaptability (the ability for a single model to make accurate predictions for a specific vertical). We demonstrate the efficacy of our methods through extensive experimentation for 11 verticals.

This is joint work with Fernando Diaz and Jean-Francois Paiement from Yahoo! Labs and will be presented at SIGIR 2010.

Tuesday, July 6, 2010

Grace Yang -- Friday July 9th

Time: Noon
Location: GHC 4405

Speaker: Grace Hui Yang, Language Technologies Institute, School of Computer Science, CMU

Title: Collecting High Quality Overlapping Labels at Low Cost.

This paper studies quality of human labels used to train search engines’ rankers. Our specific focus is performance improvements obtained by using overlapping relevance labels, which is by collecting multiple human judgments for each training sample. The paper explores whether, when, and for which samples one should obtain overlapping training labels, as well as how many labels per sample are needed. The proposed selective labeling scheme collects additional labels only for a subset of training samples, specifically for those that are labeled relevant by a judge. Our experiments show that this labeling scheme improves the NDCG of two Web search rankers on several real-world test sets, with a low labeling overhead of around 1.4 labels per sample. This labeling scheme also outperforms several methods of using overlapping labels, such as simple k-overlap, majority vote, the highest labels, etc. Finally, the paper presents a study of how many overlapping labels are needed to get the best improvement in retrieval accuracy.

This paper is published in Proceedings of the 33th Annual ACM SIGIR Conference (SIGIR2010), Geneva, Switzerland, July 19-23, 2010.