Document retrieval

About: Document retrieval is a research topic. Over the lifetime, 6821 publications have been published within this topic receiving 214383 citations.

Conceptual modeling of multimedia documents

Abstract: An approach to the document-retrieval problem that aims to increase the efficiency and effectiveness of document-retrieval systems by exploiting the semantic contents of the documents is presented. The document retrieval problem is delineated, and conceptual document modeling basics and requirements are discussed. An experimental system, the Multimedia Office Server (Multos), which implements some of the document-model concepts described, is presented. >

Automatic MeSH term assignment and quality assessment.

Abstract: For computational purposes documents or other objects are most often represented by a collection of individual attributes that may be strings or numbers. Such attributes are often called features and success in solving a given problem can depend critically on the nature of the features selected to represent documents. Feature selection has received considerable attention in the machine learning literature. In the area of document retrieval we refer to feature selection as indexing. Indexing has not traditionally been evaluated by the same methods used in machine learning feature selection. Here we show how indexing quality may be evaluated in a machine learning setting and apply this methodology to results of the Indexing Initiative at the National Library of Medicine.

Calendar incorporating document retrieval interface

Abstract: A calendar-based user interface for electronically stored documents is provided by virtue of the present invention. Each document has an associated time. A calendar view is constructed including an icon representing a particular document. The icon is presented at a location corresponding to the time of the document. In one embodiment, the calendar view includes a month view with each date having a corresponding area for presentation of document icons for documents processed on that date.

Natural language processing for online applications

Abstract: This text covers the technologies of document retrieval, information extraction, and text categorization in a way which highlights commonalities in terms of both general principles and practical concerns. It assumes some mathematical background on the part of the reader, but the chapters typically begin with a non-mathematical account of the key issues. Current research topics are covered only to the extent that they are informing current applications; detailed coverage of longer term research and more theoretical treatments should be sought elsewhere. There are many pointers at the ends of the chapters that the reader can follow to explore the literature. However, the book does maintain a strong emphasis on evaluation in every chapter both in terms of methodology and the results of controlled experimentation.