Query reformulation techniques based on query logs have been studied as a method of capturing user intent and improving retrieval effectiveness. The evaluation of these techniques has primarily, however, focused on proprietary query logs and selected samples of queries. In this paper, we suggest that anchor text, which is readily available, can be an effective substitute for a query log and study the effectiveness of a range of query reformulation techniques (including log-based stemming, substitution, and expansion) using standard TREC collections. Our results show that logbased query reformulation techniques are indeed effective with standard collections, but expansion is a much safer form of query modification than word substitution. We also show that using anchor text as a simulated query log is as least as effective as a real log for these techniques.

Current approaches for search result diversification have been categorized as either implicit or explicit. The implicit approach assumes each document represents its own topic, and promotes diversity by selecting documents for different topics based on the difference of their vocabulary. On the other hand, the explicit approach models the set of query topics, or aspects. While the former approach is generally less effective, the latter usually depends on a manually created description of the query aspects, the automatic construction of which has proven difficult. This paper introduces a new approach: term-level diversification. Instead of modeling the set of query aspects, which are typically represented as coherent groups of terms, our approach uses terms without the grouping. Our results on the ClueWeb collection show that the grouping of topic terms provides very little benefit to diversification compared to simply using the terms themselves. Consequently, we demonstrate that term-level diversification, with topic terms identified automatically from the search results using a simple greedy algorithm, significantly outperforms methods that attempt to create a full topic structure for diversification.

Abstract. Current learning to rank approaches commonly focus on learning the best possible ranking function given a small fixed set of documents. This document set is often retrieved from the collection using a simple unsupervised bag-of-words method, e.g. BM25. This can potentially lead to learning a sub-optimal ranking, since many relevant documents may be excluded from the initially retrieved set. In this paper we propose a novel two-stage learning framework to address this problem. We first learn a ranking function over the entire retrieval collection using a limited set of textual features including weighted phrases, proximities and expansion terms. This function is then used to retrieve the best possible subset of documents over which the final model is trained using a larger set of query-and document-dependent features. Empirical evaluation using two web collections unequivocally demonstrates that our proposed two-stage framework, being able to learn its model from more relevant documents, outperforms current learning to rank approaches.

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We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.