Up-cycling Data for Natural Language Generation

Abstract

Museums and other cultural heritage institutions have large databases of information about the objects in their collections, and existing Natural Language Generation (NLG) systems can generate fluent and adaptive texts for visitors, given appropriate input data, but there is typically a large amount of expert human effort required to bridge the gap between the available and the required data. We describe automatic processes which aim to significantly reduce the need for expert input during the conversion and up-cycling process. We detail domain-independent techniques for processing and enhancing data into a format which allows an existing NLG system to create adaptive texts. First we normalize the dates and names which occur in the data, and we link to the Semantic Web to add extra object descriptions. Then we use Semantic Web queries combined with a wide coverage grammar of English to extract relations which can be used to express the content of database fields in language accessible to a general user. As our test domain we use a database from the Edinburgh Musical Instrument Museum.

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