International audienceThe paradigm of Graph Rewriting is used very little in the field of Natural Language Processing. But graphs are a natural way of representing the deep syntax and the semantics of natural languages. Deep syntax is an abstraction of syntactic dependencies towards semantics in the form of graphs and there is a compact way of representing the semantics in an underspecified logical framework also with graphs. Then, Graph Rewriting reconciles efficiency with linguistic readability for producing representations at some linguistic level by transformation of a neighbor level: from raw text to surface syntax, from surface syntax to deep syntax, from deep syntax to underspecified logical semantics and conversely
AbstractConceptual graphs are a semantic representation that has a direct mapping to natural languag...
In this paper, we suggest a class of (attributed) expansive graph grammars which generate languages ...
International audienceSo far, a very large amount of work in Natural Language Processing (NLP) rely ...
In natural language processing (NLP) there is an increasing interest in formal models for processing...
In natural language processing (NLP) there is an increasing interest in formal models for processing...
Graphs are a powerful representation formalism that can be applied to a variety of aspects related t...
In natural language processing (NLP) there is an increasing interest in formal models for processing...
Directed graphs are an intuitive and versatile representation of natural language meaning because th...
In natural language processing (NLP) there is an increasing interest in formal models for pro-cessin...
So far, a very large amount of work in Natural Language Processing (NLP) rely on trees as the core m...
This paper gives some examples of how computation in a number of languages may be described as graph...
Over the last few years, a number of ar-eas of natural language processing have begun applying graph...
Thesis (Ph. D.)--University of Rochester. Department of Computer Science, 2018.In recent years, ther...
Graph grammars are graph replacement systems and can be therefore regarded as a generalization of we...
Graph grammars are graph replacement systems and can be therefore regarded as a generalization of we...
AbstractConceptual graphs are a semantic representation that has a direct mapping to natural languag...
In this paper, we suggest a class of (attributed) expansive graph grammars which generate languages ...
International audienceSo far, a very large amount of work in Natural Language Processing (NLP) rely ...
In natural language processing (NLP) there is an increasing interest in formal models for processing...
In natural language processing (NLP) there is an increasing interest in formal models for processing...
Graphs are a powerful representation formalism that can be applied to a variety of aspects related t...
In natural language processing (NLP) there is an increasing interest in formal models for processing...
Directed graphs are an intuitive and versatile representation of natural language meaning because th...
In natural language processing (NLP) there is an increasing interest in formal models for pro-cessin...
So far, a very large amount of work in Natural Language Processing (NLP) rely on trees as the core m...
This paper gives some examples of how computation in a number of languages may be described as graph...
Over the last few years, a number of ar-eas of natural language processing have begun applying graph...
Thesis (Ph. D.)--University of Rochester. Department of Computer Science, 2018.In recent years, ther...
Graph grammars are graph replacement systems and can be therefore regarded as a generalization of we...
Graph grammars are graph replacement systems and can be therefore regarded as a generalization of we...
AbstractConceptual graphs are a semantic representation that has a direct mapping to natural languag...
In this paper, we suggest a class of (attributed) expansive graph grammars which generate languages ...
International audienceSo far, a very large amount of work in Natural Language Processing (NLP) rely ...