Human–computer information retrieval

About: Human–computer information retrieval is a research topic. Over the lifetime, 6871 publications have been published within this topic receiving 195816 citations.

Search Engines: Information Retrieval in Practice

Abstract: KEY BENEFIT: Written by a leader in the field of information retrieval, this text provides the background and tools needed to evaluate, compare and modify search engines. KEY TOPICS: Coverage of the underlying IR and mathematical models reinforce key concepts. Numerous programming exercises make extensive use of Galago, a Java-based open source search engine. MARKET: A valuable tool for search engine and information retrieval professionals.

Ask for information retrieval: part I.: background and theory

Abstract: We report the results of a British Library Research and Development Department‐funded design study for an interactive information retrieval system which will determine structural representations of the anomalous states of knowledge (ASKs) underlying information needs, and attempt to resolve the anomalies through a variety of retrieval strategies performed on a database of documents represented in compatible structural formats. Part I discusses the background to the project and the theory underlying it, Part II (next issue) presents our methods, results and conclusions. Basic premises of the project were: that information needs are not in principle precisely specifiable; that it is possible to elicit problem statements from information system users from which representations of the ASK underlying the need can be derived; that there are classes of ASKs; and, that all elements of information retrieval systems ought to be based on the user's ASK. We have developed a relatively freeform interview technique for eliciting problem statements, and a statistical word co‐occurrence analysis for deriving network representations of the problem statements and abstracts. Structural characteristics of the representations have been used to determine classes of ASKs, and both ASK and information structures have been evaluated by, respectively, users and authors. Some results are: that interviewing appears to be a satisfactory technique for eliciting problem statements from which ASKs can be determined; that the statistical analysis produces structures which are generally appropriate both for documents and problem statements; that ASKs thus represented can be usefully classified according to their structural characteristics; and, that of thirty‐five subjects, only two had ASKs for which traditional ‘best match’ retrieval would be intuitively appropriate. The results of the design study indicate that at least some of our premises are reasonable, and that an ASK‐based information retrieval system is at least feasible.

Automatic information organization and retrieval

Abstract: Spend your time even for only few minutes to read a book. Reading a book will never reduce and waste your time to be useless. Reading, for some people become a need that is to do every day such as spending time for eating. Now, what about you? Do you like to read a book? Now, we will show you a new book enPDFd automatic information organization and retrieval that can be a new way to explore the knowledge. When reading this book, you can get one thing to always remember in every reading time, even step by step.

Placing search in context: the concept revisited

Abstract: Keyword-based search engines are in widespread use today as a popular means for Web-based information retrieval. Although such systems seem deceptively simple, a considerable amount of skill is required in order to satisfy non-trivial information needs. This paper presents a new conceptual paradigm for performing search in context, that largely automates the search process, providing even non-professional users with highly relevant results. This paradigm is implemented in practice in the IntelliZap system, where search is initiated from a text query marked by the user in a document she views, and is guided by the text surrounding the marked query in that document (“the context”). The context-driven information retrieval process involves semantic keyword extraction and clustering to automatically generate new, augmented queries. The latter are submitted to a host of general and domain-specific search engines. Search results are then semantically reranked, using context. Experimental results testify that using context to guide search, effectively offers even inexperienced users an advanced search tool on the Web.