This chapter is about natural language texts generated automatically. We will discuss the motivation as to why these texts are needed and discuss which domains will profit from such texts. During recent years NLG technology has matured. Millions of news articles are generated daily. However, there is potential for higher quality and more elaborated styles. For this purpose, new techniques have to be developed. These techniques need to be semantically driven. As examples, we will discuss (a) the use and the retrieval of background information by following paths in knowledge graphs, (b) how to calculate and exploit information structure, and (c) how to hyper-personalise automatically generated news
1) Natural Language Generation (NLG) Systems which take information from some database and figure ou...
The research field of Natural Language Generation offers practitioners a wide range of techniques fo...
Computer-based generation of natural language requires consideration of two different types of probl...
Natural Language Generation (NLG) is defined as the systematic approach for producing human understa...
<F4.589e+05> Automatic natural language generation from underlying representations of informa...
Natural Language Generation (NLG) -- also known as Automatic Text Generation -- is the computational...
In this paper, we are discussing the basic concepts and fundamentals of Natural Language Generation,...
Natural Language Generation (NLG) is the process of generating natural language text from an input, ...
The automatic generation of natural language messages (or NLG) has been em-ployed in many computer s...
Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often c...
In this paper, we introduce an NLG application for the automatic creation of ready-to-publish texts ...
With software automatically producing texts in natural language from structured data, the evolution ...
The common themes that these articles explore are: how to build large-coverage dictionaries and morp...
Automatic Text Generation (ATG) is a Natural Language Processing (NLP) task that aims at writing acc...
Natural Language Generation (nlg) systems generate texts in English and other human languages from n...
1) Natural Language Generation (NLG) Systems which take information from some database and figure ou...
The research field of Natural Language Generation offers practitioners a wide range of techniques fo...
Computer-based generation of natural language requires consideration of two different types of probl...
Natural Language Generation (NLG) is defined as the systematic approach for producing human understa...
<F4.589e+05> Automatic natural language generation from underlying representations of informa...
Natural Language Generation (NLG) -- also known as Automatic Text Generation -- is the computational...
In this paper, we are discussing the basic concepts and fundamentals of Natural Language Generation,...
Natural Language Generation (NLG) is the process of generating natural language text from an input, ...
The automatic generation of natural language messages (or NLG) has been em-ployed in many computer s...
Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often c...
In this paper, we introduce an NLG application for the automatic creation of ready-to-publish texts ...
With software automatically producing texts in natural language from structured data, the evolution ...
The common themes that these articles explore are: how to build large-coverage dictionaries and morp...
Automatic Text Generation (ATG) is a Natural Language Processing (NLP) task that aims at writing acc...
Natural Language Generation (nlg) systems generate texts in English and other human languages from n...
1) Natural Language Generation (NLG) Systems which take information from some database and figure ou...
The research field of Natural Language Generation offers practitioners a wide range of techniques fo...
Computer-based generation of natural language requires consideration of two different types of probl...