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.

Text Mining by Pseudo-Natural Language Understanding

2011 ◽  
pp. 1942-1946 ◽  
Author(s):  
Ruqian Lu
Keyword(s):  
Text Mining ◽  
Natural Language ◽  
Knowledge Acquisition ◽  
Information Gain ◽  
Natural Language Understanding ◽  
Language Understanding ◽  
Trade Off ◽  
Chinese Academy Of Sciences ◽  
Automatic Knowledge Acquisition ◽  
Academy Of Sciences

Text mining by pseudo natural language understanding (TM by PNLU for short) is a technique developed by the AST group of Chinese Academy of Sciences, as part of the project automatic knowledge acquisition by PNLU, which introduces a partial parse technique to avoid the difficulty of full NLU. It consists of three parts: PNL design, PNL parser implementation and PNLU based automatic knowledge acquisition. Its essence is twofold: a trade-off between information gain and feasibility of parsing, and a rational work division between human and computer.

Issues in the Structuring and Acquisition of an Ontology for Medical Language Understanding

1995 ◽  
Vol 34 (01/02) ◽  
pp. 15-24 ◽  
Author(s):  
B. Bachimont ◽  
J. Bouaud ◽  
J. Charlet ◽  
J.-F. Boisvieux ◽  
P. Zweigenbaum
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Knowledge Acquisition ◽  
Domain Knowledge ◽  
Natural Language Understanding ◽  
Conceptual Representation ◽  
Medical Texts ◽  
Language Understanding ◽  
Medical Language

Abstract:Medical natural language understanding basically aims at representing the contents of medical texts in a formal, conceptual representation. The understanding process itself increasingly relies on a body of domain knowledge, generally expressed in the same conceptual formalism. The design of such a conceptual representation is a key knowledge-acquisition issue. When representing knowledge, the most important point is to ensure that the formal exploitation of the knowledge representation conforms to its meaning in the domain. We examined some methodological and theoretical principles to enforce this conformity. These principles result from our experience in MENELAS, a medical language understanding project.

A knowledge engineer based on natural language understanding for an expert system: DOMES

1991 ◽  
Vol 5 (2) ◽  
pp. 97-107
Author(s):  
B. Yang ◽  
P. Datseris
Keyword(s):  
Expert System ◽  
Natural Language ◽  
Knowledge Acquisition ◽  
Rhode Island ◽  
Effective Means ◽  
Natural Language Understanding ◽  
Design Criteria ◽  
Language Understanding ◽  
Knowledge Engineer ◽  
Knowledge Based

In order to enhance the knowledge acquisition capability of the expert system DOMES (Design Of Mechanisms by an Expert System), which is developed at the University of Rhode Island for creative type synthesis of mechanisms, methodologies have been developed to build a knowledge engineer module based on natural language understanding. Specifically, artificial intelligence concepts and Lisp programming techniques have been incorporated in this module to implement the following: (1) analysing and understanding design criteria supplied by human designers in the form of English sentences; and (2) transforming these design criteria into Lisp code and storing them in the knowledge base of DOMES. This knowledge engineer module enhances the capability and improves the performance of the expert system DOMES by providing an effective means for knowledge acquisition based on natural language understanding. The concepts and implementation techniques in developing this module are general and can also be utilized for other knowledge-based systems.

Automated Coding of Patient Discharge Summaries Using Conceptual Graphs

1995 ◽  
Vol 34 (04) ◽  
pp. 345-351 ◽  
Author(s):  
A. Burgun ◽  
L. P. Seka ◽  
D. Delamarre ◽  
P. Le Beux
Keyword(s):  
Natural Language ◽  
Patient Discharge ◽  
Natural Language Understanding ◽  
Assessment Procedure ◽  
Coding System ◽  
Language Understanding ◽  
Processing Scheme ◽  
Automated Coding ◽  
Conceptual Graph ◽  
Discharge Summaries

Abstract:In medicine, as in other domains, indexing and classification is a natural human task which is used for information retrieval and representation. In the medical field, encoding of patient discharge summaries is still a manual time-consuming task. This paper describes an automated coding system of patient discharge summaries from the field of coronary diseases into the ICD-9-CM classification. The system is developed in the context of the European AIM MENELAS project, a natural-language understanding system which uses the conceptual-graph formalism. Indexing is performed by using a two-step processing scheme; a first recognition stage is implemented by a matching procedure and a secondary selection stage is made according to the coding priorities. We show the general features of the necessary translation of the classification terms in the conceptual-graph model, and for the coding rules compliance. An advantage of the system is to provide an objective evaluation and assessment procedure for natural-language understanding.

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