J. Shane Culpepper

Including summaries in system evaluation

In batch evaluation of retrieval systems, performance is calculated based on predetermined relevance judgements applied to a list of documents returned by the system for a query. This evaluation paradigm, however, ignores the current standard operation of search systems which require the user to view summaries of documents prior to reading the documents themselves. In this paper we modify the popular IR metrics MAP and P@10 to incorporate the summary reading step of the search process, and study the effects on system rankings using TREC data. Based on a user study, we establish likely disagreements between relevance judgements of summaries and of documents, and use these values to seed simulations of summary relevance in the TREC data. Re-evaluating the runs submitted to the TREC Web Track, we find the average correlation between system rankings and the original TREC rankings is 0.8 (Kendall τ), which is lower than commonly accepted for system orderings to be considered equivalent. The system that has the highest MAP in TREC generally remains amongst the highest MAP systems when summaries are taken into account, but other systems become equivalent to the top ranked system depending on the simulated summary relevance. Given that system orderings alter when summaries are taken into account, the small amount of effort required to judge summaries in addition to documents (19 seconds vs 88 seconds on average in our data) should be undertaken when constructing test collections.

Top-k ranked document search in general text databases

Text search engines return a set of k documents ranked by similarity to a query. Typically, documents and queries are drawn from natural language text, which can readily be partitioned into words, allowing optimizations of data structures and algorithms for ranking. However, in many new search domains (DNA, multimedia, OCR texts, Far East languages) there is often no obvious definition of words and traditional indexing approaches are not so easily adapted, or break down entirely. We present two new algorithms for ranking documents against a query without making any assumptions on the structure of the underlying text. We build on existing theoretical techniques, which we have implemented and compared empirically with new approaches introduced in this paper. Our best approach is significantly faster than existing methods in RAM, and is even three times faster than a state-of-the-art inverted file implementation for English text when word queries are issued.

Efficient in-memory top-k document retrieval

For over forty years the dominant data structure for ranked document retrieval has been the inverted index. Inverted indexes are effective for a variety of document retrieval tasks, and particularly efficient for large data collection scenarios that require disk access and storage. However, many efficiency-bound search tasks can now easily be supported entirely in memory as a result of recent hardware advances. In this paper we present a hybrid algorithmic framework for in-memory bag of-words ranked document retrieval using a self-index derived from the FM-Index, wavelet tree, and the compressed suffix tree data structures, and evaluate the various algorithmic trade-offs for performing efficient queries entirely in-memory. We compare our approach with two classic approaches to bag-of-words queries using inverted indexes, term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing. We show that our framework is competitive with state-of-the-art indexing structures, and describe new capabilities provided by our algorithms that can be leveraged by future systems to improve effectiveness and efficiency for a variety of fundamental search operations.

The effect of pooling and evaluation depth on IR metrics

Batch IR evaluations are usually performed in a framework that consists of a document collection, a set of queries, a set of relevance judgments, and one or more effectiveness metrics. A large number of evaluation metrics have been proposed, with two primary families having emerged: recall-based metrics, and utility-based metrics. In both families, the pragmatics of forming judgments mean that it is usual to evaluate the metric to some chosen depth such as

Open source information retrieval: a report on the SIGIR 2012 workshop

k=20

Efficient and effective realtime prediction of drive-by download attacks

k=20 or

A Comparison of Document-at-a-Time and Score-at-a-Time Query Evaluation

k=100