tailieunhanh - Báo cáo khoa học: "Using Non-lexical Features to Identify Effective Indexing Terms for Biomedical Illustrations"

Automatic image annotation is an attractive approach for enabling convenient access to images found in a variety of documents. Since image captions and relevant discussions found in the text can be useful for summarizing the content of images, it is also possible that this text can be used to generate salient indexing terms. Unfortunately, this problem is generally domainspecific because indexing terms that are useful in one domain can be ineffective in others. Thus, we present a supervised machine learning approach to image annotation utilizing non-lexical features1 extracted from image-related text to select useful terms. We apply this approach. | Using Non-lexical Features to Identify Effective Indexing Terms for Biomedical Illustrations Matthew Simpson Dina Demner-Fushman Charles Sneiderman Sameer K. Antani George R. Thoma Lister Hill National Center for Biomedical Communications National Library of Medicine NIH Bethesda MD USA simpsonmatt ddemner csneiderman santani gthoma @ Abstract Automatic image annotation is an attractive approach for enabling convenient access to images found in a variety of documents. Since image captions and relevant discussions found in the text can be useful for summarizing the content of images it is also possible that this text can be used to generate salient indexing terms. Unfortunately this problem is generally domainspecific because indexing terms that are useful in one domain can be ineffective in others. Thus we present a supervised machine learning approach to image annotation utilizing non-lexical features1 extracted from image-related text to select useful terms. We apply this approach to several subdomains of the biomedical sciences and show that we are able to reduce the number of ineffective indexing terms. 1 Introduction Authors of biomedical publications often utilize images and other illustrations to convey information essential to the article and to support and reinforce textual content. These images are useful in support of clinical decisions in rich document summaries and for instructional purposes. The task of delivering these images and the publications in which they are contained to biomedical clinicians and researchers in an accessible way is an information retrieval problem. Current research in the biomedical domain . Antani et al. 2008 Florea et al. 2007 has investigated hybrid approaches to image retrieval combining elements of content-based image retrieval CBIR and annotation-based image retrieval ABIR . ABIR compared to the image- 1 Non-lexical features describe attributes of image-related text but not the text itself . unlike a .

• Báo cáo khoa học
• Using Non lexical Features
• Identify Effective Indexing Terms
• Biomedical Illustrations
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln