Index ordering by query-independent measures

Conventional approaches to information retrieval search through all applicable entries in an inverted file for a particular collection in order to find those documents with the highest scores. For particularly large collections this may be extremely time consuming. A solution to this problem is to only search a limited amount of the collection at query-time, in order to speed up the retrieval process. In doing this we can also limit the loss in retrieval efficacy (in terms of accuracy of results). The way we achieve this is to firstly identify the most “important” documents within the collection, and sort documents within inverted file lists in order of this “importance”. In this way we limit the amount of information to be searched at query time by eliminating documents of lesser importance, which not only makes the search more efficient, but also limits loss in retrieval accuracy. Our experiments, carried out on the TREC Terabyte collection, report significant savings, in terms of number of postings examined, without significant loss of effectiveness when based on several measures of importance used in isolation, and in combination. Our results point to several ways in which the computation cost of searching large collections of documents can be significantly reduced

A comparison of evaluation measures given how users perform on search tasks

Information retrieval has a strong foundation of empirical investigation: based on the position of relevant resources in a ranked answer list, a variety of system performance metrics can be calculated. One of the most widely reported measures, mean average precision (MAP), provides a single numerical value that aims to capture the overall performance of a retrieval system. However, recent work has suggested that broad measures such as MAP do not relate to actual user performance on a number of search tasks. In this paper, we investigate the relationship between various retrieval metrics, and consider how these reflect user search performance. Our results suggest that there are two distinct categories of measures: those that focus on high precision in an answer list, and those that attempt to capture a broader summary, for example by including a recall component. Analysis of runs submitted to the TREC terabyte track in 2006 suggests that the relative performance of systems can differ significantly depending on which group of measures is being used

Enhancing access to the Bibliome: the TREC 2004 Genomics Track

BACKGROUND: The goal of the TREC Genomics Track is to improve information retrieval in the area of genomics by creating test collections that will allow researchers to improve and better understand failures of their systems. The 2004 track included an ad hoc retrieval task, simulating use of a search engine to obtain documents about biomedical topics. This paper describes the Genomics Track of the Text Retrieval Conference (TREC) 2004, a forum for evaluation of IR research systems, where retrieval in the genomics domain has recently begun to be assessed. RESULTS: A total of 27 research groups submitted 47 different runs. The most effective runs, as measured by the primary evaluation measure of mean average precision (MAP), used a combination of domain-specific and general techniques. The best MAP obtained by any run was 0.4075. Techniques that expanded queries with gene name lists as well as words from related articles had the best efficacy. However, many runs performed more poorly than a simple baseline run, indicating that careful selection of system features is essential. CONCLUSION: Various approaches to ad hoc retrieval provide a diversity of efficacy. The TREC Genomics Track and its test collection resources provide tools that allow improvement in information retrieval systems

The adventures of Miranda in the brave new world: learning in a Web 2.0 millennium

This paper looks at the implications of Web 2.0 technologies for university teaching and learning. The latest generation of undergraduates already live in a Web 2.0 world. They have new service expectations and are increasingly dissatisfied with teacher‐centred pedagogies. To attract and retain these students, universities will need to rethink their operations. New social technologies mean that universities have the chance to create a new generation of student‐centred learning environments, to realize the idea of a University 2.0. The following discussion draws upon a fictional character in order to capture the possible futures of such a brave new world

Multi-Stage Search Architectures for Streaming Documents

The web is becoming more dynamic due to the increasing engagement and contribution of Internet users in the age of social media. A more dynamic web presents new challenges for web search--an important application of Information Retrieval (IR). A stream of new documents constantly flows into the web at a high rate, adding to the old content. In many cases, documents quickly lose their relevance. In these time-sensitive environments, finding relevant content in response to user queries requires a real-time search service; immediate availability of content for search and a fast ranking, which requires an optimized search architecture. These aspects of today's web are at odds with how academic IR researchers have traditionally viewed the web, as a collection of static documents. Moreover, search architectures have received little attention in the IR literature. Therefore, academic IR research, for the most part, does not provide a mechanism to efficiently handle a high-velocity stream of documents, nor does it facilitate real-time ranking. This dissertation addresses the aforementioned shortcomings. We present an efficient mech- anism to index a stream of documents, thereby enabling immediate availability of content. Our indexer works entirely in main memory and provides a mechanism to control inverted list con- tiguity, thereby enabling faster retrieval. Additionally, we consider document ranking with a machine-learned model, dubbed "Learning to Rank" (LTR), and introduce a novel multi-stage search architecture that enables fast retrieval and allows for more design flexibility. The stages of our architecture include candidate generation (top k retrieval), feature extraction, and docu- ment re-ranking. We compare this architecture with a traditional monolithic architecture where candidate generation and feature extraction occur together. As we lay out our architecture, we present optimizations to each stage to facilitate low-latency ranking. These optimizations include a fast approximate top k retrieval algorithm, document vectors for feature extraction, architecture- conscious implementations of tree ensembles for LTR using predication and vectorization, and algorithms to train tree-based LTR models that are fast to evaluate. We also study the efficiency- effectiveness tradeoffs of these techniques, and empirically evaluate our end-to-end architecture on microblog document collections. We show that our techniques improve efficiency without degrading quality

Index ordering by query-independent measures

There is an ever-increasing amount of data that is being produced from various data sources — this data must then be organised effectively if we hope to search though it. Traditional information retrieval approaches search through all available data in a particular collection in order to find the most suitable results, however, for particularly large collections this may be extremely time consuming. Our purposed solution to this problem is to only search a limited amount of the collection at query-time, in order to speed this retrieval process up. Although, in doing this we aim to limit the loss in retrieval efficacy (in terms of accuracy of results). The way we aim to do this is to firstly identify the most “important” documents within the collection, and then sort the documents within the collection in order of their "importance” in the collection. In this way we can choose to limit the amount of information to search through, by eliminating the documents of lesser importance, which should not only make the search more efficient, but should also limit any loss in retrieval accuracy. In this thesis we investigate various different query-independent methods that may indicate the importance of a document in a collection. The more accurate the measure is at determining an important document, the more effectively we can eliminate documents from the retrieval process - improving the query-throughput of the system, as well as providing a high level of accuracy in the returned results. The effectiveness of these approaches are evaluated using the datasets provided by the terabyte track at the Text REtreival Conference (TREC)

User Variability and IR System Evaluation

ABSTRACT Test collection design eliminates sources of user variability to make statistical comparisons among information retrieval (IR) systems more affordable. Does this choice unnecessarily limit generalizability of the outcomes to real usage scenarios? We explore two aspects of user variability with regard to evaluating the relative performance of IR systems, assessing effectiveness in the context of a subset of topics from three TREC collections, with the embodied information needs categorized against three levels of increasing task complexity. First, we explore the impact of widely differing queries that searchers construct for the same information need description. By executing those queries, we demonstrate that query formulation is critical to query effectiveness. The results also show that the range of scores characterizing effectiveness for a single system arising from these queries is comparable or greater than the range of scores arising from variation among systems using only a single query per topic. Second, our experiments reveal that searchers display substantial individual variation in the numbers of documents and queries they anticipate needing to issue, and there are underlying significant differences in these numbers in line with increasing task complexity levels. Our conclusion is that test collection design would be improved by the use of multiple query variations per topic, and could be further improved by the use of metrics which are sensitive to the expected numbers of useful documents

Detection and management of redundancy for information retrieval

The growth of the web, authoring software, and electronic publishing has led to the emergence of a new type of document collection that is decentralised, amorphous, dynamic, and anarchic. In such collections, redundancy is a significant issue. Documents can spread and propagate across such collections without any control or moderation. Redundancy can interfere with the information retrieval process, leading to decreased user amenity in accessing information from these collections, and thus must be effectively managed. The precise definition of redundancy varies with the application. We restrict ourselves to documents that are co-derivative: those that share a common heritage, and hence contain passages of common text. We explore document fingerprinting, a well-known technique for the detection of co-derivative document pairs. Our new lossless fingerprinting algorithm improves the effectiveness of a range of document fingerprinting approaches. We empirically show that our algorithm can be highly effective at discovering co-derivative document pairs in large collections. We study the occurrence and management of redundancy in a range of application domains. On the web, we find that document fingerprinting is able to identify widespread redundancy, and that this redundancy has a significant detrimental effect on the quality of search results. Based on user studies, we suggest that redundancy is most appropriately managed as a postprocessing step on the ranked list and explain how and why this should be done. In the genomic area of sequence homology search, we explain why the existing techniques for redundancy discovery are increasingly inefficient, and present a critique of the current approaches to redundancy management. We show how document fingerprinting with a modified version of our algorithm provides significant efficiency improvements, and propose a new approach to redundancy management based on wildcards. We demonstrate that our scheme provides the benefits of existing techniques but does not have their deficiencies. Redundancy in distributed information retrieval systems - where different parts of the collection are searched by autonomous servers - cannot be effectively managed using traditional fingerprinting techniques. We thus propose a new data structure, the grainy hash vector, for redundancy detection and management in this environment. We show in preliminary tests that the grainy hash vector is able to accurately detect a good proportion of redundant document pairs while maintaining low resource usage