This paper discusses the nature, history and current characteristics of Language Engineering, which is contrasted with Natural Language Processing and Computational Linguistics, and which is shown to have attained its own distinct identity in recent years. Major trends in the field are examined, including its focus on large-scale practical tasks and on quantitative evaluation of progress, and its willingness to embrace a diverse range of techniques. The importance of software engineering in this context is noted, as are some sociological aspects of the practitioner group.

The identification and extraction of technical terms is one of the better understood and most robust Natural Language Processing (NLP) technologies within the current state of the art of language engineering. In generic information management contexts, terms have been used primarily for procedures seeking to identify a set of phrases that is useful for tasks such as text indexing, computational lexicology, and machine-assisted translation: such tasks make important use of the assumption that terminology is representative of a given domain. This paper discusses an extension of basic terminology identification technology for the application to two higher level semantic tasks: domain description, the specification of the technical domain of a document, and content characterisation, the construction of a compact, coherent and useful representation of the topical content of a text. With these extensions, terminology identification becomes the foundation of an operational environment for document processing and content abstraction.

We argue that grammatical analysis is a viable alternative to concept spotting for processing spoken input in a practical spoken dialogue system. We discuss the structure of the grammar, and a model for robust parsing which combines linguistic sources of information and statistical sources of information. We discuss test results suggesting that grammatical processing allows fast and accurate processing of spoken input.

Verbmobil represents a new generation of speech-to-speech translation systems in which spontaneously spoken language, speaker independence and adaptability as well as the combination of deep and shallow approaches to the analysis and transfer problems are the main features. The project brought together researchers from the fields of signal processing, computational linguistics and artificial intelligence. Verbmobil goes beyond the state-of-the-art in each of these areas, but its main achievement is the seamless integration of them. The first project phase (1993–1996) has been followed up by the second project phase (1997–2000), which aims at applying the results to further languages and at integrating innovative telecooperation techniques. Quite apart from the speech and language processing issues, the size and complexity of the project represent an extreme challenge on the areas of project management and software engineering:

[bull ] 50 researchers from 29 organizations at different sites in different countries are involved in the software development process,

[bull ] to reuse existing software, hardware, knowledge and experience, only a few technical restrictions could be given to the partners.

In this article we describe the Verbmobil prototype system from a software-engineering perspective. We discuss:

[bull ] the modularized functional architecture,

[bull ] the flexible and extensible software architecture which reflects that functional architecture,

[bull ] the evolutionary process of system integration,

[bull ] the communication-based organizational structure of the project,

[bull ] the evaluation of the system operational by the end of the first project phase.