Improving Explicit Profile Acquisition by Means of Adaptive Natural Language Dialog

Interpreting Natural Language

Contemporary Psychology ◽

10.1037/026658 ◽

1987 ◽

Vol 32 (1) ◽

pp. 33-34

Author(s):

Greg N. Carlson

Keyword(s):

Natural Language

Studies in interactive communication: Limited vocabulary natural language dialogue.

PsycEXTRA Dataset ◽

10.1037/e402062004-001 ◽

1975 ◽

Cited By ~ 3

Author(s):

Michael J. Kelly

Keyword(s):

Natural Language ◽

Interactive Communication ◽

Natural Language Dialogue

Bayesian Language: Uncertainty and Inference in Natural Language

PsycEXTRA Dataset ◽

10.1037/e502412013-280 ◽

2012 ◽

Author(s):

Noah Goodman

Keyword(s):

Natural Language

A Change in Focus: How Readers Process Natural Language Quantifiers

PsycEXTRA Dataset ◽

10.1037/e524912015-244 ◽

2014 ◽

Author(s):

Sri Siddhi Upadhyay ◽

Celia Klin

Keyword(s):

Natural Language

Abstraction in Natural Language Categories

PsycEXTRA Dataset ◽

10.1037/e557102013-070 ◽

2012 ◽

Author(s):

Loes Stukken ◽

Wouter Voorspoels ◽

Gert Storms ◽

Wolf Vanpaemel

Keyword(s):

Natural Language

Conversational re-prompting in natural language dialog

PsycEXTRA Dataset ◽

10.1037/e577222012-001 ◽

2004 ◽

Author(s):

Harry E. Blanchard ◽

Osamuyimen T. Stewart

Keyword(s):

Natural Language

Natural Language Processing and Enhanced Clinical Decision Making Radiology and VINCI

PsycEXTRA Dataset ◽

10.1037/e615572012-015 ◽

2012 ◽

Author(s):

Eliot Siegel

Keyword(s):

Decision Making ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Clinical Decision Making ◽

Clinical Decision

Natural Language Processing in the Clinical Setting

PsycEXTRA Dataset ◽

10.1037/e615572012-013 ◽

2012 ◽

Author(s):

Thomas H. Payne

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Clinical Setting

From NLP to Natural Language Understanding for medical decision making

PsycEXTRA Dataset ◽

10.1037/e615572012-006 ◽

2012 ◽

Author(s):

Chitta Baral

Keyword(s):

Decision Making ◽

Natural Language ◽

Medical Decision Making ◽

Natural Language Understanding ◽

Medical Decision ◽

Language Understanding

An Automated Clinical Information System

Methods of Information in Medicine ◽

10.1055/s-0038-1636234 ◽

1964 ◽

Vol 3 (02) ◽

pp. 45-50 ◽

Cited By ~ 6

Author(s):

D. Yoder ◽

R. Swearingen ◽

E. Schenthal ◽

W. Sweeney ◽

J. Nettleton

Keyword(s):

Natural Language ◽

Clinical Information System ◽

Drug Evaluation ◽

Scientific Information ◽

Clinical Information ◽

Clinical Situation ◽

Field Variable ◽

Record System ◽

New Members ◽

Clinical Terminology

An automated clinical record system must have the following characteristics: as far as the physician is concerned it must operate in natural language on standard sized paper; it must be able to accept information from the physician at a time when he is oriented to clinical terminology and a clinical mode of thinking; it must have an output which is clinically useful for the care and management of a patient; each item of information must be addressable so that it may act as an index for scientific information retrieval; it must be capable of accepting quantative and natural language information.Clinical information constitutes a mathematical set, only a few members of which are applicable to any particular clinical situation, and to which new members are constantly being added. The members of this set are seldom mutually exclusive. An acceptable system which is capable of processing this type of information has been designed utilizing the concepts of self-encoding forms and variable-field, variable-length records. Applications of these principles will expedite hospital automation, the establishment of drug evaluation information systems, and of regional and nationwide medical record systems.