Many concept-to-text generation systems require domain-specific linguistic resources to produce high quality texts, but manually constructing these resources can be tedious and costly. Focusing on NaturalOWL, a publicly available state of the art natural language generator for OWL ontologies, we propose methods to extract from the Web sentence plans and natural language names, two of the most important types of domain-specific linguistic resources used by the generator. Experiments show that texts generated using linguistic resources extracted by our methods in a semi-automatic manner, with minimal human involvement, are perceived as being almost as good as texts generated using manually authored linguistic resources, and much better than texts produced by using linguistic resources extracted from the relation and entity identifiers of the ontology.
Generating text in a hypermedia environment places different demands on a text generation system than occurs in non-interactive environments. This paper describes some of these demands, then shows how the architecture of one text generation system, ILEX, has been shaped by them. The architecture is described in terms of the levels of linguistic representation used, and the processes which map between them. Particular attention is paid to the processes of content selection and text structuring. Figures Preface 1. Introduction 2. National Language Generation in practice 3. The architecture of a Natural Language Generation system 4. Document planning 5. Microplanning 6. Surface realisation 7. Beyond text generation Appendix References Index. We present the source authoring facilities of a natural language generation system that produces personalised descriptions of objects in multiple natural languages starting from language-independent symbolic information in ontologies and databases as well as pieces of canned text. The system has been tested in applications ranging from museum exhibitions to presentations of computer equipment for sale. We discuss the architecture of the overall system, the resources that the authors manipulate, the functionality of the authoring facilities, the system’s personalisation mechanisms, and how they relate to source authoring. A usability evaluation of the authoring facilities is also presented, followed by more recent work on reusing information extracted from existing databases and documents, and supporting the owl ontology specification language. The authors describe a system that generates descriptions of museum objects tailored to the user. The texts presented to adults, children, and experts differ in several ways, from the choice of words used to the complexity of the sentence forms. M-PIRO can currently generate text in three languages: English, Greek, and Italian. The grammar resources are language independent as much as possible. M-PIRO's system architecture is significantly more modular than that of its predecessor ILEX. In particular, the linguistic resources, database, and user-modeling subsystems are now separate from the systems that perform the natural language generation and speech synthesis. This paper presents the ONTOSUM system which uses Natural Language Generation (NLG) techniques to produce textual summaries from Semantic Web ontologies. The main contribution of this work is in showing how existing NLG tools can be adapted to Semantic Web ontologies, in a way which minimises the customisation effort while offering more diverse output than template-based ontology verbalisers. A novel dimension of this work is the focus on tailoring the summary formatting and length according to a device profile (e.g., mobile phone, Web browser). Another innovative idea is the use of ontology mapping for summary generation from different ontologies. We present the RAGS (Reference Architecture for Generation Systems) framework: a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.