Linguistic Knowledge Representation and Automatic Acquisition Based on a Combination of Ontology with Statistical Method

2006 ◽  
pp. 637-649
Author(s):  
Dequan Zheng ◽  
Tiejun Zhao ◽  
Sheng Li ◽  
Hao Yu
Keyword(s):  
Knowledge Representation ◽  
Statistical Method ◽  
Linguistic Knowledge ◽  
Automatic Acquisition

FIXED PHRASE SCHEME AS A SPECIAL LINGUISTIC KNOWLEDGE FORMAT

2020 ◽  
pp. 43-55
Author(s):  
V.Y. Melikyan ◽  
◽  
A.V. Melikyan ◽  
V.V. Posidelova ◽  
◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Linguistic Knowledge ◽  
Language Level ◽  
Grammatical Variation ◽  
Limited Possibility ◽  
Ontological Aspect ◽  
Logical Semantic

The article focuses on the theoretical and practical aspects of logical-semantic and grammatical-communicative division of the fixed phrase scheme structure. The authors argue that a fixed phrase scheme is dominated by a plan of content over the plan of expression, and therefore generally the gnoseological aspect prevails. The syntactic construction phraseologization expands its possibilities in cognitive and communicative aspects: the number of represented typical objective situations, typical sentence meanings, types of relations, and therefore systems of relations between minimal segments of thought increases. At the same time, the orientation of relations between minimal segments of thought becomes stable, which is due to the limited possibility of a fixed phrase scheme in terms of grammatical variation. The phraseologization degree determines the cognitive and communicative parameters of the fixed phrase scheme: the higher the phraseologization degree, the more significant is the influence of the gnoseological aspect; the lower, the more the influence of the ontological aspect is manifested. The phraseologization phenomenon determines the representation possibility of a wider knowledge format that the fixed phrase scheme possesses in comparison with a free syntactic construction. Thus, in the fixed phrase scheme sphere, there is a different correlation between the linguistic knowledge format and the form of this knowledge representation at the language level. All this allows us to consider the fixed phrase scheme as a particular format of linguistic knowledge.

Alternative Ways for Knowledge Collection, Indexing and Robust Language Retrieval

1998 ◽  
Vol 37 (04/05) ◽  
pp. 315-326 ◽  
Author(s):  
C. Lovis ◽  
A.-M. Rassinoux ◽  
J.-R. Scherrer ◽  
R. H. Baud
Keyword(s):  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Building Blocks ◽  
Linguistic Knowledge ◽  
Recent Experience ◽  
Medical Texts ◽  
Natural Components

AbstractDefinitions are provided of the key entities in knowledge representation for Natural Language Processing (NLP). Starting from the words, which are the natural components of any sentence, both the role of expressions and the decomposition of words into their parts are emphasized. This leads to the notion of concepts, which are either primitive or composite depending on the model where they are created. The problem of finding the most adequate degree of granularity for a concept is studied. From this reflection on basic Natural Language Processing components, four categories of linguistic knowledge are recognized, that are considered to be the building blocks of a Medical Linguistic Knowledge Base (MLKB). Following on the tracks of a recent experience in building a natural language-based patient encoding browser, a robust method for conceptual indexing and query of medical texts is presented with particular attention to the scheme of knowledge representation.

The Russian prefix pod- from the viewpoint of lexical concepts

2014 ◽  
Vol 6 (4) ◽  
pp. 462-491 ◽  
Author(s):  
JOHANNA VIIMARANTA
Keyword(s):  
Knowledge Representation ◽  
Dynamic Process ◽  
Conceptual Models ◽  
Word Meaning ◽  
Vertical Movement ◽  
Cognitive Models ◽  
Linguistic Knowledge ◽  
Lexical Concepts ◽  
Permanent Property ◽  
National Corpus

abstractThe Russian prefix pod- has several meanings, both concrete ones having to do with approaching or being under or down, and a series of seemingly unrelated abstract meanings such as imitating, ingratiating, or doing in secret. This paper approaches the polysemy of pod- from the viewpoint of the Theory of Lexical Concepts and Conceptual Models (LCCM) that sees word meaning not as a permanent property of words, but as a dynamic process in which context and accessed non-linguistic knowledge representation play an important role. This approach uses the notion of lexical concepts to describe the mediating unit between concrete linguistic examples and cognitive models that these examples are connected to. The 505 verbs analyzed bring up the lexical concepts [UNDER], [VERTICAL MOVEMENT], [CLOSE], and [CONTACT]. The connection of these lexical concepts with certain metaphorical and metonymical models is also discussed. Twelve of the 505 verbs are examined more closely in different contexts with the help of twenty-nine illustrative examples from the spoken corpus of the Russian National Corpus.

Reductionism and Knowledge Representation : Can They Be Integrated?

2004 ◽  
Vol 49 (5) ◽  
pp. 580-581
Author(s):  
Arthur B. Markman
Keyword(s):  
Knowledge Representation

The elements of statistical method.

1916 ◽  
Author(s):  
Willford I. King
Keyword(s):  
Statistical Method

The Role of Systematicity in the Consolidation of Linguistic Knowledge

2014 ◽  
Author(s):  
Gareth Gaskell ◽  
Jelena Mirkovic
Keyword(s):  
Linguistic Knowledge

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.

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