Acquiring Correct Knowledge for Natural Language Generation

Acquiring Correct Knowledge for Natural Language Generation

Acquiring Correct Knowledge for Natural Language Generation

Natural language generation (nlg) systems are computer software systems that produce texts in English and other human languages, often from non-linguistic input data. nlg systems, like most ai systems, need substantial amounts of knowledge. However, our experience in two nlg projects suggests that it is difficult to acquire correct knowledge for nlg systems; indeed, every knowledge acquisition ...

Knowledge Structures for Natural Language Generation

The development of natural language interfaces to Artificial Intelligence systems is dependent on the representation of knowledge. A major impediment to building such systems has been the difficulty in adding sufficient linguistic and conceptual knowledge to extend and adapt their capabilities. This difficulty has been apparent in systems which perform the task of language production, i. e. the...

Knowledge Acquisition for Natural Language Generation

We describe the knowledge acquisition (KA) techniques used to build the STOP system, especially sorting and think-aloud protocols. That is, we describe the ways in which we interacted with domain experts to determine appropriate user categories, schemas, detailed content rules, and so forth for STOP. Informal evaluations of these techniques suggest that they had some benefit, but perhaps were m...

Narratological Knowledge for Natural Language Generation

The paper proposes an architecture for advanced NLG systems that handle narratives. Special attention is paid to document planning. Domain modelling and meta-knowledge modelling for a narratological structurer are exemplified.