The distinction between linguistic and conceptual semantics in medical terminology and its implication for NLP-based knowledge acquisition (original) (raw)
Related papers
1997
Natural language understanding systems have to exploit various kinds of knowledge to be able 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. In this paper, we describe how the distinction between conceptual and linguistic semantics probably can assist in reaching this objective, provided that distinguishing between them is not done to rigorously. We present several toy examples to support this view and argue that in a multilingual environment linguistic ontologies should be designed as interfaces between domain conceptualisations and linguistic knowledge bases.
Proceedings / AMIA ... Annual Symposium. AMIA Symposium, 2000
This research project presents methodological and theoretical issues related to the inter-relationship between linguistic and conceptual semantics, analysing the results obtained by the application of a NLP parser to a set of radiology reports. Our objective is to define a technique for associating linguistic methods with domain specific ontologies for semi-automatic extraction of intermediate representation (IR) information formats and medical ontological knowledge from clinical texts. We have applied the Edinburgh LTG natural language parser to 2810 clinical narratives describing radiology procedures. In a second step, we have used medical expertise and ontology formalism for identification of semantic structures and abstraction of IR schemas related to the processed texts. These IR schemas are an association of linguistic and conceptual knowledge, based on their semantic contents. This methodology aims to contribute to the elaboration of models relating linguistic and logical con...
Issues in the Structuring and Acquisition of an Ontology for Medical Language Understanding
Methods of Information in Medicine, 1995
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. In this paper, we examine some methodological and theoretical principles to enforce this conformity. These principles result from our experience in Menelas, a medical language understanding project. 1
Building Medical Ontologies Based on Terminology Extraction from Texts: Methodological Propositions
2005
In the medical field, it is now established that the maintenance of unambiguous thesauri is accomplished by the building of ontologies. Our task in the PertoMed project is to help pneumologists code acts and diagnoses with a software that represents medical knowledge by an ontology of the concerned specialty. We apply natural language processing tools to corpora to develop the resources needed to build this ontology. In this paper, our objective is to develop a methodology for the knowledge engineer to build various types of medical ontologies based on terminology extraction from texts according to the differential semantics theory. Our main research hypothesis concerns the joint use of two methods: distributional analysis and recognition of semantic relationships by lexico-syntactic patterns. The expected result is the building of an ontology of pneumology.
Cognitive Approach to Medical Semantics Description and Interpretation
International Conference of Informatics and Systems, 2008
In the paper will be presented new achievements of the authors obtained in the field of development some new cognitive information systems, performed the tasks of automatic understanding of medical image semantics. Such systems are dedicated to implementation of the machine semantics understanding paradigm for selected image types, with special regards to various medical visualization. The practical development of such understanding systems is possible owing to defining computer procedures of cognitive inference, as used by the developed new classes of intelligent systems for pattern recognition and semantic reasoning. In particular, we shall present a new class of cognitive UBIAS systems introduced by the authors for the interpretation of biomedical patterns.
Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation
International Journal of Environmental Research and Public Health
Extracting clinical concepts, such as problems, diagnosis, and treatment, from unstructured clinical narrative documents enables data-driven approaches such as machine and deep learning to support advanced applications such as clinical decision-support systems, the assessment of disease progression, and the intelligent analysis of treatment efficacy. Various tools such as cTAKES, Sophia, MetaMap, and other rules-based approaches and algorithms have been used for automatic concept extraction. Recently, machine- and deep-learning approaches have been used to extract, classify, and accurately annotate terms and phrases. However, the requirement of an annotated dataset, which is labor-intensive, impedes the success of data-driven approaches. A rule-based mechanism could support the process of annotation, but existing rule-based approaches fail to adequately capture contextual, syntactic, and semantic patterns. This study intends to introduce a comprehensive rule-based system that automa...
Automatic knowledge acquisition from medical texts
Proceedings of the AMIA …, 1996
An approach to knowledge-based understanding ofrealistic texts from the medical domain (viz. findings of gastro-intestinal diseases) is presented. We survey majormethodologicalfeatures ofan object-oriented,fully lexicalized, dependency-based grammar model which is tightly linked to domain knowledge representations based on description logics. The parser adheres to the principles of robustness, incrementality and concurrency. The substrate ofautomatic knowledge acquisition are text knowledge bases generated by the parser from medical narratives, which represent major portions ofthe content ofthese documents.
Formalizing Biomedical Concepts from Textual Definitions
Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions.
A natural language understanding system for knowledge-based analysis of medical texts
2004
An approach to knowledge-based understanding of real-world texts from the medical domain (viz. gastro-intestinal findings) is presented. We survey major methodological features of an object-oriented, fully lexicalized, dependency-based grammar model which is tightly linked to domain knowledge representations based on description logics. The parser adheres to the principles of robustness, incrementality and concurrency. The substrate of automatic knowledge acquisition are text knowledge bases generated by the parser from medical narratives, which represent major portions of the content of these documents.
Methodology to build medical ontology from textual resources
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2006
In the medical field, it is now established that the maintenance of unambiguous thesauri goes through ontologies. Our research task is to help pneumologists code acts and diagnoses with a software that represents medical knowledge through a domain ontology. In this paper, we describe our general methodology aimed at knowledge engineers in order to build various types of medical ontologies based on terminology extraction from texts. The hypothesis is to apply natural language processing tools to textual patient discharge summaries to develop the resources needed to build an ontology in pneumology. Results indicate that the joint use of distributional analysis and lexico-syntactic patterns performed satisfactorily for building such ontologies.