An edited volume containing data structures and algorithms for information retrieved including a disk with examples written in C. For programmers and students interested in parsing text, automated indexing, its the first collection in book form of the basic data structures and algorithms that are critical to the storage and retrieval of documents.

https://hornad.fei.tuke.sk/~genci/Vyucba/OOBDS/Archiv/Podklady/TExtoveIS&IR/Information%20Retrieval%20Data%20Structures%20&%20Algorithms.PDF

Information Retrieval: Data Structures and Algorithms

Automatic query expansion has long been suggested as a technique for dealing with the fundamental issue of word mismatch in information retrieval. A number of approaches to expansion have been studied and, more recently, attention has focused on techniques that analyze the corpus to discover word relationship (global techniques) and those that analyze documents retrieved by the initial query ( local feedback). In this paper, we compare the effectiveness of these approaches and show that, although global analysis haa some advantages, local analysia is generally more effective. We also show that using global analysis techniques.

/pdf/quary-expansion-using-local-and-global-document-analysis-1cb5v2zqss.pdf

Quary Expansion Using Local and Global Document Analysis

The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date. In this tutorial, we introduce the key techniques in the area, describing both a core implementation and how the core can be enhanced through a range of extensions. We conclude with a comprehensive bibliography of text indexing literature.

/pdf/inverted-files-for-text-search-engines-1fgfjm96s3.pdf

Inverted files for text search engines

The relative ineffectiveness of information retrieval systems is largely caused by the inaccuracy with which a query formed by a few keywords models the actual user information need. One well known method to overcome this limitation is automatic query expansion (AQE), whereby the user’s original query is augmented by new features with a similar meaning. AQE has a long history in the information retrieval community but it is only in the last years that it has reached a level of scientific and experimental maturity, especially in laboratory settings such as TREC. This survey presents a unified view of a large number of recent approaches to AQE that leverage various data sources and employ very different principles and techniques. The following questions are addressed. Why is query expansion so important to improve search effectiveness? What are the main steps involved in the design and implementation of an AQE component? What approaches to AQE are available and how do they compare? Which issues must still be resolved before AQE becomes a standard component of large operational information retrieval systems (e.g., search engines)?

/pdf/a-survey-of-automatic-query-expansion-in-information-30cfvdlacg.pdf

A Survey of Automatic Query Expansion in Information Retrieval

A protein molecule is a covalent chain of amino acid residues. Although it is topologically linear, in physiological conditions it folds into a unique (though flexible) three-dimensional structure. This structure, which has been determined by x-ray crystallography and nuclear magnetic resonance for many proteins (Bernstein et al., 1977; Abola et al., 1987), is referred to as the native structure. As demonstrated by the experiments of Anfinsen and co-workers (Anfinsen et al., 1961; Anfinsen, 1973), at least some protein molecules, when denatured (unfolded) by disrupting conditions in their environment (such as acidity or high temperature) can spontaneously refold to their native structures when proper physiological conditions are restored. Thus, all of the information necessary to determine the native structure can be contained in the amino acid sequence.

Computational Complexity, Protein Structure Prediction, and the Levinthal Paradox

AaSTRACT. In a full-text natural-language retrieval system, local feedbacl~ is the process of formulating a new ~mproved search based on clustering terms from the documents returned m a previous search of any given query Experiments were run on a database of US patents It ~s concluded that m contrast toglobalclustermg, w h e r e the size of matrices hmmts apphcatmns to small databases and improvements are doubtful, local clustering is practical also for large databases and appears to improve overall performance, especially tf metrical constraints and weighting by proximity are embedded m the local feedback The local methods adapt themselves to each mdwtdual search and produce useful searchonyms terms which are \"synonymous\" m the context of one query Searchonyms lead to new ~mproved search formulahons both via manual and vm automahc feedback

Local Feedback in Full-Text Retrieval Systems

(1985). Systems of Numeration. The American Mathematical Monthly: Vol. 92, No. 2, pp. 105-114.

Aviezri S. Fraenkel

Papers

Local Feedback in Full-Text Retrieval Systems

Systems of Numeration

How Many Squares Can a String Contain

Computing a perfect strategy for n × n chess requires time exponential in n

Complexity of protein folding