Tree-like dispositional dependency structures for non-propositional semantic inferencing. On a SCIP approach to natural language understanding by machine
Identifieur interne : 000F91 ( PascalFrancis/Corpus ); précédent : 000F90; suivant : 000F92Tree-like dispositional dependency structures for non-propositional semantic inferencing. On a SCIP approach to natural language understanding by machine
Auteurs : B. B. RiegerSource :
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
Arguing for the semiotic modeling of natural language understanding by machine is to follow a procedural stance of approach focusing on processes of meaning constitution. These can be typified in pragmatic situations of performative language games which may be analyzed empirically, described formally, and simulated computationally. In doing so, graph theoretical tools have been employed and new tree structures developed which allow both, to restrict the relational manifold in high-dimensional vector space structures computed as fuzzy word meaning representations, and to visualize semantically motivated relevancies emerging from such restrictions as reflexive, non-symmetric, and (weakly) transitive dependency relations among them. As a basal, context-sensitive form of reorganizing distributionally represented fuzzy entities, the tree like dispositional dependency structures (DDS) serve as a non-propositional format for conceptual associations and semantic inferencing by machine, as opposed to propositional reasoning based on truth-functional constraints. After a short introduction into semiotic cognitive information processing (SCIP) and the text analyzing and meaning representational formalisms employed, DDS tree generation will be discussed, and some examples be given to illustrate the algorithms' semantic inferencing potential as computed from and performed on a sample of German newspaper texts.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
| pA |
|
|---|
Format Inist (serveur)
| NO : | PASCAL 99-0518051 INIST |
|---|---|
| FT : | Traitement d'information et gestion d'incertitudes dans les systèmes à base de connaissances |
| ET : | Tree-like dispositional dependency structures for non-propositional semantic inferencing. On a SCIP approach to natural language understanding by machine |
| AU : | RIEGER (B. B.) |
| AF : | FB II: Department of Computational Linguistics, University of Trier/Allemagne (1 aut.) |
| DT : | Congrès; Niveau analytique |
| SO : | Information processing and management of uncertainty in knowledge-based systems. International conference/7/1998-07-06/Paris FRA; France; Paris: EDK; Da. 1998; Pp. 351-358; ISBN 2-84254-013-1 |
| LA : | Anglais |
| EA : | Arguing for the semiotic modeling of natural language understanding by machine is to follow a procedural stance of approach focusing on processes of meaning constitution. These can be typified in pragmatic situations of performative language games which may be analyzed empirically, described formally, and simulated computationally. In doing so, graph theoretical tools have been employed and new tree structures developed which allow both, to restrict the relational manifold in high-dimensional vector space structures computed as fuzzy word meaning representations, and to visualize semantically motivated relevancies emerging from such restrictions as reflexive, non-symmetric, and (weakly) transitive dependency relations among them. As a basal, context-sensitive form of reorganizing distributionally represented fuzzy entities, the tree like dispositional dependency structures (DDS) serve as a non-propositional format for conceptual associations and semantic inferencing by machine, as opposed to propositional reasoning based on truth-functional constraints. After a short introduction into semiotic cognitive information processing (SCIP) and the text analyzing and meaning representational formalisms employed, DDS tree generation will be discussed, and some examples be given to illustrate the algorithms' semantic inferencing potential as computed from and performed on a sample of German newspaper texts. |
| CC : | 001D02C02 |
| FD : | Système intelligent; Système apprentissage; Système information; Système base connaissances; Linguistique mathématique; Analyse sémantique |
| ED : | Intelligent system; Learning systems; Information system; Knowledge based systems; Computational linguistics; Semantic analysis |
| SD : | Sistema inteligente; Sistema información; Linguística matemática; Análisis semántico |
| LO : | INIST-Y 32217.354000084546550490 |
| ID : | 99-0518051 |
Links to Exploration step
Pascal:99-0518051Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Tree-like dispositional dependency structures for non-propositional semantic inferencing. On a SCIP approach to natural language understanding by machine</title><author><name sortKey="Rieger, B B" sort="Rieger, B B" uniqKey="Rieger B" first="B. B." last="Rieger">B. B. Rieger</name><affiliation><inist:fA14 i1="01"><s1>FB II: Department of Computational Linguistics, University of Trier</s1><s3>DEU</s3><sZ>1 aut.</sZ></inist:fA14></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">99-0518051</idno><date when="1998">1998</date><idno type="stanalyst">PASCAL 99-0518051 INIST</idno><idno type="RBID">Pascal:99-0518051</idno><idno type="wicri:Area/PascalFrancis/Corpus">000F91</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Tree-like dispositional dependency structures for non-propositional semantic inferencing. On a SCIP approach to natural language understanding by machine</title><author><name sortKey="Rieger, B B" sort="Rieger, B B" uniqKey="Rieger B" first="B. B." last="Rieger">B. B. Rieger</name><affiliation><inist:fA14 i1="01"><s1>FB II: Department of Computational Linguistics, University of Trier</s1><s3>DEU</s3><sZ>1 aut.</sZ></inist:fA14></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Computational linguistics</term><term>Information system</term><term>Intelligent system</term><term>Knowledge based systems</term><term>Learning systems</term><term>Semantic analysis</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Système intelligent</term><term>Système apprentissage</term><term>Système information</term><term>Système base connaissances</term><term>Linguistique mathématique</term><term>Analyse sémantique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Arguing for the semiotic modeling of natural language understanding by machine is to follow a procedural stance of approach focusing on processes of meaning constitution. These can be typified in pragmatic situations of performative language games which may be analyzed empirically, described formally, and simulated computationally. In doing so, graph theoretical tools have been employed and new tree structures developed which allow both, to restrict the relational manifold in high-dimensional vector space structures computed as fuzzy word meaning representations, and to visualize semantically motivated relevancies emerging from such restrictions as reflexive, non-symmetric, and (weakly) transitive dependency relations among them. As a basal, context-sensitive form of reorganizing distributionally represented fuzzy entities, the tree like dispositional dependency structures (DDS) serve as a non-propositional format for conceptual associations and semantic inferencing by machine, as opposed to propositional reasoning based on truth-functional constraints. After a short introduction into semiotic cognitive information processing (SCIP) and the text analyzing and meaning representational formalisms employed, DDS tree generation will be discussed, and some examples be given to illustrate the algorithms' semantic inferencing potential as computed from and performed on a sample of German newspaper texts.</div></front></TEI><inist><standard h6="B"><pA><fA08 i1="01" i2="1" l="ENG"><s1>Tree-like dispositional dependency structures for non-propositional semantic inferencing. On a SCIP approach to natural language understanding by machine</s1></fA08><fA09 i1="01" i2="1" l="ENG"><s1>IPMU : information processing and management of uncertainty in knowledge-based systems : Paris, 6-10 July 1998</s1></fA09><fA09 i1="02" i2="5" l="FRE"><s1>Traitement d'information et gestion d'incertitudes dans les systèmes à base de connaissances</s1></fA09><fA11 i1="01" i2="1"><s1>RIEGER (B. B.)</s1></fA11><fA14 i1="01"><s1>FB II: Department of Computational Linguistics, University of Trier</s1><s3>DEU</s3><sZ>1 aut.</sZ></fA14><fA20><s1>351-358</s1></fA20><fA21><s1>1998</s1></fA21><fA23 i1="01"><s0>ENG</s0></fA23><fA25 i1="01"><s1>EDK</s1><s2>Paris</s2></fA25><fA26 i1="01"><s0>2-84254-013-1</s0></fA26><fA30 i1="01" i2="1" l="ENG"><s1>Information processing and management of uncertainty in knowledge-based systems. International conference</s1><s2>7</s2><s3>Paris FRA</s3><s4>1998-07-06</s4></fA30><fA30 i1="02" i2="1" l="FRE"><s1>Traitement d'information et gestion d'incertitudes dans les systèmes à base de connaissances. Conférence internationale</s1><s2>7</s2><s3>Paris FRA</s3><s4>1998-07-06</s4></fA30><fA43 i1="01"><s1>INIST</s1><s2>Y 32217</s2><s5>354000084546550490</s5></fA43><fA44><s0>0000</s0><s1>© 1999 INIST-CNRS. All rights reserved.</s1></fA44><fA45><s0>28 ref.</s0></fA45><fA47 i1="01" i2="1"><s0>99-0518051</s0></fA47><fA60><s1>C</s1></fA60><fA61><s0>A</s0></fA61><fA66 i1="01"><s0>FRA</s0></fA66><fC01 i1="01" l="ENG"><s0>Arguing for the semiotic modeling of natural language understanding by machine is to follow a procedural stance of approach focusing on processes of meaning constitution. These can be typified in pragmatic situations of performative language games which may be analyzed empirically, described formally, and simulated computationally. In doing so, graph theoretical tools have been employed and new tree structures developed which allow both, to restrict the relational manifold in high-dimensional vector space structures computed as fuzzy word meaning representations, and to visualize semantically motivated relevancies emerging from such restrictions as reflexive, non-symmetric, and (weakly) transitive dependency relations among them. As a basal, context-sensitive form of reorganizing distributionally represented fuzzy entities, the tree like dispositional dependency structures (DDS) serve as a non-propositional format for conceptual associations and semantic inferencing by machine, as opposed to propositional reasoning based on truth-functional constraints. After a short introduction into semiotic cognitive information processing (SCIP) and the text analyzing and meaning representational formalisms employed, DDS tree generation will be discussed, and some examples be given to illustrate the algorithms' semantic inferencing potential as computed from and performed on a sample of German newspaper texts.</s0></fC01><fC02 i1="01" i2="X"><s0>001D02C02</s0></fC02><fC03 i1="01" i2="X" l="FRE"><s0>Système intelligent</s0><s5>01</s5></fC03><fC03 i1="01" i2="X" l="ENG"><s0>Intelligent system</s0><s5>01</s5></fC03><fC03 i1="01" i2="X" l="SPA"><s0>Sistema inteligente</s0><s5>01</s5></fC03><fC03 i1="02" i2="1" l="FRE"><s0>Système apprentissage</s0><s5>02</s5></fC03><fC03 i1="02" i2="1" l="ENG"><s0>Learning systems</s0><s5>02</s5></fC03><fC03 i1="03" i2="X" l="FRE"><s0>Système information</s0><s5>03</s5></fC03><fC03 i1="03" i2="X" l="ENG"><s0>Information system</s0><s5>03</s5></fC03><fC03 i1="03" i2="X" l="SPA"><s0>Sistema información</s0><s5>03</s5></fC03><fC03 i1="04" i2="1" l="FRE"><s0>Système base connaissances</s0><s5>04</s5></fC03><fC03 i1="04" i2="1" l="ENG"><s0>Knowledge based systems</s0><s5>04</s5></fC03><fC03 i1="05" i2="X" l="FRE"><s0>Linguistique mathématique</s0><s5>05</s5></fC03><fC03 i1="05" i2="X" l="ENG"><s0>Computational linguistics</s0><s5>05</s5></fC03><fC03 i1="05" i2="X" l="SPA"><s0>Linguística matemática</s0><s5>05</s5></fC03><fC03 i1="06" i2="X" l="FRE"><s0>Analyse sémantique</s0><s5>06</s5></fC03><fC03 i1="06" i2="X" l="ENG"><s0>Semantic analysis</s0><s5>06</s5></fC03><fC03 i1="06" i2="X" l="SPA"><s0>Análisis semántico</s0><s5>06</s5></fC03><fN21><s1>333</s1></fN21></pA></standard><server><NO>PASCAL 99-0518051 INIST</NO><FT>Traitement d'information et gestion d'incertitudes dans les systèmes à base de connaissances</FT><ET>Tree-like dispositional dependency structures for non-propositional semantic inferencing. On a SCIP approach to natural language understanding by machine</ET><AU>RIEGER (B. B.)</AU><AF>FB II: Department of Computational Linguistics, University of Trier/Allemagne (1 aut.)</AF><DT>Congrès; Niveau analytique</DT><SO>Information processing and management of uncertainty in knowledge-based systems. International conference/7/1998-07-06/Paris FRA; France; Paris: EDK; Da. 1998; Pp. 351-358; ISBN 2-84254-013-1</SO><LA>Anglais</LA><EA>Arguing for the semiotic modeling of natural language understanding by machine is to follow a procedural stance of approach focusing on processes of meaning constitution. These can be typified in pragmatic situations of performative language games which may be analyzed empirically, described formally, and simulated computationally. In doing so, graph theoretical tools have been employed and new tree structures developed which allow both, to restrict the relational manifold in high-dimensional vector space structures computed as fuzzy word meaning representations, and to visualize semantically motivated relevancies emerging from such restrictions as reflexive, non-symmetric, and (weakly) transitive dependency relations among them. As a basal, context-sensitive form of reorganizing distributionally represented fuzzy entities, the tree like dispositional dependency structures (DDS) serve as a non-propositional format for conceptual associations and semantic inferencing by machine, as opposed to propositional reasoning based on truth-functional constraints. After a short introduction into semiotic cognitive information processing (SCIP) and the text analyzing and meaning representational formalisms employed, DDS tree generation will be discussed, and some examples be given to illustrate the algorithms' semantic inferencing potential as computed from and performed on a sample of German newspaper texts.</EA><CC>001D02C02</CC><FD>Système intelligent; Système apprentissage; Système information; Système base connaissances; Linguistique mathématique; Analyse sémantique</FD><ED>Intelligent system; Learning systems; Information system; Knowledge based systems; Computational linguistics; Semantic analysis</ED><SD>Sistema inteligente; Sistema información; Linguística matemática; Análisis semántico</SD><LO>INIST-Y 32217.354000084546550490</LO><ID>99-0518051</ID></server></inist></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Rhénanie/explor/UnivTrevesV1/Data/PascalFrancis/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000F91 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Corpus/biblio.hfd -nk 000F91 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Rhénanie
|area= UnivTrevesV1
|flux= PascalFrancis
|étape= Corpus
|type= RBID
|clé= Pascal:99-0518051
|texte= Tree-like dispositional dependency structures for non-propositional semantic inferencing. On a SCIP approach to natural language understanding by machine
}}
| This area was generated with Dilib version V0.6.31. |  |