A term normalization method for efficient knowledge acquisition through text processing

2012 ◽  
Vol 65 (1) ◽  
pp. 75-91 ◽  
Author(s):  
Myunggwon Hwang ◽  
Do-Heon Jeong ◽  
Jinhyung Kim ◽  
Sa-Kwang Song ◽  
Hanmin Jung ◽  
...  
Keyword(s):  
Knowledge Acquisition ◽  
Text Processing ◽  
Normalization Method

scholarly journals Predicate word dictionary as a tool for automatic text processing by situational-relational analysis

2021 ◽  
Author(s):  
Valery A. Mishlanov ◽  
◽  
Liudmila А. Kadzhaya ◽  
Vladimir A. Salimovskiy ◽  
Ivan V. Smirnov ◽  
...  
Keyword(s):  
Knowledge Acquisition ◽  
Text Analysis ◽  
Text Processing ◽  
Relational Analysis ◽  
Dictionary Structure ◽  
Automatic Text

This paper discusses the issues of improving a predicate word dictionary structure that is used in solving problems of knowledge acquisition and text analysis. The principle of open dictionary architecture is shown. It takes into account the stylistic differentiation of speech and involves the description of predicate word subsystems functioning in separate speech varieties.

Differential Mnemonic Effects of Text Processing Strategies for Good

2001 ◽  
Author(s):  
Robert J. Hines ◽  
Mark A. McDaniel ◽  
Melissa Guynn
Keyword(s):  
Text Processing ◽  
Processing Strategies

Text processing as a function of layout in a videotex system.

1983 ◽  
Author(s):  
Kjell Ohlsson ◽  
Lars-Goeran Nilsson ◽  
Jerker Roennberg
Keyword(s):  
Text Processing

Memorial Influences on the Fluency of Text Processing

2007 ◽  
Author(s):  
Matthew Collins ◽  
Betty Ann Levy
Keyword(s):  
Text Processing

Organizational signals and reading instructions affect text processing

1991 ◽  
Author(s):  
Elizabeth Pugzles Lorch ◽  
Robert F. Lorch ◽  
Jonathan M. Golding
Keyword(s):  
Text Processing

The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
Author(s):  
F. Buekens ◽  
G. De Moor ◽  
A. Waagmeester ◽  
W. Ceusters
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Natural Language Understanding ◽  
Knowledge Bases ◽  
Linguistic Knowledge ◽  
Medical Terminology ◽  
Language Understanding ◽  
Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

Structured Data Entry for Reliable Acquisition of Pharmacokinetic Data

1996 ◽  
Vol 35 (03) ◽  
pp. 261-264 ◽  
Author(s):  
T. Schromm ◽  
T. Frankewitsch ◽  
M. Giehl ◽  
F. Keller ◽  
D. Zellner
Keyword(s):  
Text Processing ◽  
Data Entry ◽  
Processing System ◽  
Database System ◽  
Structured Data ◽  
Pharmacokinetic Parameters ◽  
Pharmacokinetic Data ◽  
Probability Of Error ◽  
Error Frequency ◽  
Estimated Error

Abstract:A pharmacokinetic database was constructed that is as free of errors as possible. Pharmacokinetic parameters were derived from the literature using a text-processing system and a database system. A random data sample from each system was compared with the original literature. The estimated error frequencies using statistical methods differed significantly between the two systems. The estimated error frequency in the text-processing system was 7.2%, that in the database system 2.7%. Compared with the original values in the literature, the estimated probability of error for identical pharmacokinetic parameters recorded in both systems is 2.4% and is not significantly different from the error frequency in the database. Parallel data entry with a text-processing system and a database system is, therefore, not significantly better than structured data entry for reducing the error frequency.

154. Knowledge Acquisition from a Plant'S Physical Examination Databank Via Inductive Learning

2002 ◽  
Author(s):  
S. Lin ◽  
J. Yu ◽  
S. Shieh ◽  
F. Chang ◽  
T. Li
Keyword(s):  
Physical Examination ◽  
Knowledge Acquisition ◽  
Inductive Learning
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