Search (8 results, page 1 of 1)

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Abstract

The need for effective identity matching systems has led to extensive research in the area of name search. For the most part, such work has been limited to English and other Latin-based languages. Consequently, algorithms such as Soundex and n-gram matching are of limited utility for languages such as Arabic, which has vastly different morphologic features that rely heavily on phonetic information. The dearth of work in this field is partly caused by the lack of standardized test data. Consequently, we have built a collection of 7,939 Arabic names, along with 50 training queries and 111 test queries. We use this collection to evaluate a variety of algorithms, including a derivative of Soundex tailored to Arabic (ASOUNDEX), measuring effectiveness by using standard information retrieval measures. Our results show an improvement of 70% over existing approaches.

Date

22. 7.2006 17:20:20

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Abstract

We explore unsupervised learning techniques for extracting semantic information about biomedical concepts and topics, and introduce a passage retrieval model for using these semantics in context to improve genomics literature search. Our contributions include a new passage retrieval model based on an undirected graphical model (Markov Random Fields), and new methods for modeling passage-concepts, document-topics, and passage-terms as potential functions within the model. Each potential function includes distributional evidence to disambiguate topics, concepts, and terms in context. The joint distribution across potential functions in the graph represents the probability of a passage being relevant to a biologist's information need. Relevance ranking within each potential function simplifies normalization across potential functions and eliminates the need for tuning of passage retrieval model parameters. Our dimensional indexing model facilitates efficient aggregation of topic, concept, and term distributions. The proposed passage-retrieval model improves search results in the presence of varying levels of semantic evidence, outperforming models of query terms, concepts, or document topics alone. Our results exceed the state-of-the-art for automatic document retrieval by 14.46% (0.3554 vs. 0.3105) and passage retrieval by 15.57% (0.1128 vs. 0.0976) as assessed by the TREC 2007 Genomics Track, and automatic document retrieval by 18.56% (0.3424 vs. 0.2888) as assessed by the TREC 2005 Genomics Track. Automatic document retrieval results for TREC 2007 and TREC 2005 are statistically significant at the 95% confidence level (p = .0359 and .0253, respectively). Passage retrieval is significant at the 90% confidence level (p = 0.0893).

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Abstract

Interested in how an efficient search engine works? Want to know what algorithms are used to rank resulting documents in response to user requests? The authors answer these and other key information on retrieval design and implementation questions is provided. This book is not yet another high level text. Instead, algorithms are thoroughly described, making this book ideally suited for both computer science students and practitioners who work on search-related applications. As stated in the foreword, this book provides a current, broad, and detailed overview of the field and is the only one that does so. Examples are used throughout to illustrate the algorithms. The authors explain how a query is ranked against a document collection using either a single or a combination of retrieval strategies, and how an assortment of utilities are integrated into the query processing scheme to improve these rankings. Methods for building and compressing text indexes, querying and retrieving documents in multiple languages, and using parallel or distributed processing to expedite the search are likewise described. This edition is a major expansion of the one published in 1998. Neuaufl. 2005: Besides updating the entire book with current techniques, it includes new sections on language models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.

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Abstract

Peer-to-peer (P2P) file sharing is a leading Internet application. Millions of users use P2P file-sharing systems daily to search for and download files, accounting for a large portion of Internet traffic. Due to their scale, it is important to fully understand how these systems work. We analyze user queries and shared files collected on the Gnutella system, draw some conclusions on the nature of the application, and propose some research problems.

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Abstract

We describe an unsupervised, language-independent spelling correction search system. We compare the proposed approach with unsupervised and supervised algorithms. The described approach consistently outperforms other unsupervised efforts and nearly matches the performance of a current state-of-the-art supervised approach.

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Date

17. 1.2000 12:22:18

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Abstract

The authors review a log of billions of Web queries that constituted the total query traffic for a 6-month period of a general-purpose commercial Web search service. Previously, query logs were studied from a single, cumulative view. In contrast, this study builds on the authors' previous work, which showed changes in popularity and uniqueness of topically categorized queries across the hours in a day. To further their analysis, they examine query traffic on a daily, weekly, and monthly basis by matching it against lists of queries that have been topically precategorized by human editors. These lists represent 13% of the query traffic. They show that query traffic from particular topical categories differs both from the query stream as a whole and from other categories. Additionally, they show that certain categories of queries trend differently over varying periods. The authors key contribution is twofold: They outline a method for studying both the static and topical properties of a very large query log over varying periods, and they identify and examine topical trends that may provide valuable insight for improving both retrieval effectiveness and efficiency.