Natural Language Agreement Description for Reversible Grammars

Diaconescu, Ştefan

doi:10.1007/978-3-540-24581-0_14

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…In the course of text generation, the agreement rules must indicate how the combination of the syntactic categories must be associated with the syntactic elements [7,8,21]. The generation process we propose allows obtaining a surface text based on a structured paths chain.…”

Section: Np -> Noun Noun Adj Np -> Noun Noun Np -> Noun Adjmentioning

confidence: 99%

Natural Language Agreement in the Generation Mechanism based on Stratified Graphs

Negru

Grigoraș

Dănciulescu

2015

Proceedings of the 7th Balkan Conference on Informatics Conference

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A Stratified Graph can generate natural language constructions by acting as a transition network with arc labels referring to word categories and word forms. In this representation, each grammar rule can be transposed in a sequence of labeled paths. The generation mechanism defined by means of stratified graph uses a bottom-up approach. The advantage of this generation mechanism relies on its bottom-up approach characteristic: this mechanism never suggests constructions that are not correct based on the involved representation elements. As it will be presented, implementing syntactic agreement rules in this natural language generation mechanism can determine different results even if identical words are considered in the generation mechanism.

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Section: Np -> Noun Noun Adj Np -> Noun Noun Np -> Noun Adjmentioning

confidence: 99%

Natural Language Agreement in the Generation Mechanism based on Stratified Graphs

Negru

Grigoraș

Dănciulescu

2015

Proceedings of the 7th Balkan Conference on Informatics Conference

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“…In the course of text generation, the agreement rules must indicate how the combination of the syntactic categories and values must be associated with the syntactic elements [8]. The global task of NLG is to map a given formal input onto a natural language output to achieve a given communicative goal in a specific context ( [1], [6]).…”

Section: Natural Language Generation In Labeled Stratified Graphsmentioning

confidence: 99%

Splitting the structured paths in stratified graphs. Application in Natural Language Generation

Dănciulescu

Colhon

2014

Analele Universitatii "Ovidius" Constanta - Seria Matematica

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The concept of labeled stratified graph (LSG) introduces some method of knowledge representation. The inference process developed for this structures uses the paths of the stratified graphs, an order between the elementary arcs of a path and some results of universal algebras. The order is defined by considering a structured path instead of a regular path. The application described in this paper interprets the symbolic elements of a LSG with natural language constructions. In this manner we obtained a mechanism for generation coherent texts in a natural language (for this approach, Romanian). The generation method is based on labeled stratified graph representation and the inference mechanism is guided by the structured paths of these representations.

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“…(19)(1, 2) <phrase> ::= ( "we" <verbal group>, "we"( r1(<verbal group>( )))) (20)(19,3) <phrase> ::= ( "we" <verb> <complement> <complement'>, "we"( r1( <verb> ( r2( <complement>( )), r3 ( <complement'>( ))))) (21)(20,13) <phrase> ::= ( "we" "provide" <complement> <complement'>, "we"( r1("provide"( r2( <complement>( )), r3( <complement'>( ))))) (22)(21,4) <phrase> ::= ( "we" "provide" <attribute> <noun> <complement'>, "we"( r1("provide"( r2( <noun>(r4( <attribute>( )))), r3(<complement'>( ))))) (23) (22,5) <phrase> ::= ( "we" "provide" <attribute> <noun> "for" <coordination>, "we"( r1("provide"( r2( <noun>(r4( <attribute>( )))), r3("for"( <coordination>( )))))) (24)(23,12) <phrase> ::= ( "we" "provide" "practical <noun> "for" <coordination>, "we"( r1( "provide"( r2( <noun>(r4( "practical"( )))), r3( "for"(<coordination> ( )))))) (25)(24,14) <phrase> ::= ( "we" "provide" "practical" "advice" "for" <coordination>, "we"( r1( "provide"( r2( "advice"(r4( "practical"( )))), r3( "for"( <coordination> ( )))))) (26)(25,6) <phrase> ::= ( "we" "provide" "practical" "advice"…”

Section: Generative Dependency Grammarmentioning

confidence: 99%

“…b) Dependencies: We accept here that the dependency aspects are reduced to the mode different parts of a phrase are in relation one another (coordinate and governor/subordinate relations). c) Agreement: By agreement [5] we will understand the mode different part of speech "match" one another when they are in certain dependency relations from the point of view of the values of different morphologic or syntactic categories.…”

Section: A General Structurementioning

confidence: 99%

Natural Language Syntax Description using Generative Dependency Grammar

Diaconescu¹

2008

Polibits

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Abstract-The paper presents a practical solution to describe natural language syntax. This solution is based on a Generative Dependency Grammar (GDG). A theoretical definition of these grammars and some of their proprieties is given. GDG are implemented in a declarative computer language GRAALAN (Grammar Abstract Language). The paper shortly present the features of GRAALAN and, after that, a more detailed implementation of natural language syntax description is given. GRAALAN offers for natural language syntactic description some strong features that respond to the following requests: a compact description, the possibility to express the syntax and the agreement and to specify the errors met in a text. The description has also the feature of reversibility. The paper presents some conclusions concerning the using of GRAALAN to describe the syntax (among others natural language features).

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Natural Language Agreement Description for Reversible Grammars

Cited by 4 publications

References 3 publications

Natural Language Agreement in the Generation Mechanism based on Stratified Graphs

Natural Language Agreement in the Generation Mechanism based on Stratified Graphs

Splitting the structured paths in stratified graphs. Application in Natural Language Generation

Natural Language Syntax Description using Generative Dependency Grammar

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