Unsupervised template induction over email data is a central component in applications such as information extraction, document classification, and auto-reply. The benefits of automatically generating such templates are known for structured data, e.g. machine generated HTML emails. However much less work has been done in performing the same task over unstructured email data.

Machine-generated documents such as email or dynamic web pages are single instantiations of a pre-defined structural template. As such, they can be viewed as a hierarchy of template and document specific content. This hierarchical template representation has several important advantages for document clustering and classification. First, templates capture common topics among the documents, while filtering out the potentially noisy variabilities such as personal information. Second, template representations scale far better than document representations since a single template captures numerous documents. Finally, since templates group together structurally similar documents, they can propagate properties between all the documents that match the template. In this paper, we use these advantages for document classification by formulating an efficient and effective hierarchical label propagation and discovery algorithm. The labels are propagated first over a template graph (constructed based on either term-based or topic-based similarities), and then to the matching documents. We evaluate the performance of the proposed algorithm using a large donated email corpus and show that the resulting template graph is significantly more compact than the corresponding document graph and the hierarchical label propagation is both efficient and effective in increasing the coverage of the baseline document classification algorithm. We demonstrate that the template label propagation achieves more than 91% precision and 93% recall, while increasing the label coverage by more than 11%.

With the increased availability of e-books and digitized book collections, more users are searching the web for information about books. There are many online digital libraries containing book, author and subject data, which are accessed via internal search services as well as external web sites, such as Google. Although this is a common yet complex information-seeking behavior involving multiple search systems with different characteristics, little is known about how users find information in this scenario.

Conventional retrieval systems view documents as a unit and look at different retrieval types within a document. We introduce Proteus, a frame-work for seamlessly navigating books as dynamic collections which are defined on the fly. Proteus allows us to search various retrieval types. Navigable types include pages, books, named persons, locations, and pictures in a collection of books taken from the Internet Archive. The demonstration shows the value of multi-type browsing in dynamic collections to peruse new data.

A large class of queries can be viewed as linear combinations of smaller subqueries. Additionally, many situations arise when part or all of one subquery has been preprocessed or has cached information, while another subquery requires full processing. This type of query is common, for example, in relevance feedback settings where the original query has been run to produce a set of expansion terms, but the expansion terms still need to be processed. We investigate mechanisms to reduce the time needed to process queries of this nature. We use RM3, a variant of the Relevance Model scoring algorithm, as our instantiation of this arrangement. We examine the different scenarios that can arise when we have access to the internal structure of each subquery. Given this additional information, we investigate methods to utilize this information, reducing processing costs substantially. Depending on the amount of accessibility we have into the subqueries, we can reduce processing costs over 80% without affecting the score of the final results.

We study information goals and patterns of attention in explorato-ry search for health information on the Web, reporting results of a large-scale log-based study. We examine search activity associated with the goal of diagnosing illness from symptoms versus more general information-seeking about health and illness. We decom-pose exploratory health search into evidence-based and hypothe-sis-directed information seeking. Evidence-based search centers on the pursuit of details and relevance of signs and symptoms. Hypothesis-directed search includes the pursuit of content on one or more illnesses, including risk factors, treatments, and therapies for illnesses, and on the discrimination among different diseases under the uncertainty that exists in advance of a confirmed diag-nosis. These different goals of exploratory health search are not independent, and transitions can occur between them within or across search sessions. We construct a classifier that identifies medically-related search sessions in log data. Given a set of search sessions flagged as health-related, we show how we can identify different intentions persisting as foci of attention within those sessions. Finally, we discuss how insights about foci dynamics can help us better understand exploratory health search behavior and better support health search on the Web.

Pseudo-relevance feedback (PRF) improves search quality by expanding the query using terms from high-ranking documents from an initial retrieval. Although PRF can often result in large gains in effectiveness, running two queries is time consuming, limiting its applicability. We describe a PRF method that uses corpus pre-processing to achieve query-time speeds that are near those of the original queries. Specifically, Relevance Modeling, a language modeling based PRF method, can be recast to benefit substantially from finding pairwise document relationships in advance. Using the resulting Fast Relevance Model (fastRM), we substantially reduce the online retrieval time and still benefit from expansion. We further explore methods for reducing the preprocessing time and storage requirements of the approach, allowing us to achieve up to a 10% increase in MAP over unexpanded retrieval,vwhile only requiring 1% of the time of standard expansion.

A document or web page in isolation may appear completely reasonable, but may represent a biased perspective on the topic being discussed. Given the topic of a document, we propose new metrics provocativeness and balance that suggest when the topic could be controversial. We explore the use of these metrics to characterize the subjectivity of the topics in the TREC Blog Track.

In this work, we address coreference retrieval, which involves identifying aliases that are distinct references to an entity. We begin with a known alias and discover unknown aliases that refer to the same entity. We use Entity Language Models to capture the contextual language around the known alias, which aids in finding new aliases. We also show that modeling the significant dates of the known aliases improves alias discovery performance.