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In this introductory chapter, we briefly present the context of the STEVIN programme (Sect. 1.1), in particular its rationale and main scientific goals. Subsequently, a short overview is given of all the STEVIN projects’ results (Sect. 1.2). The first subsection (1.2.1) lists the scientific projects, which will be treated in detail in the subsequent chapters of this volume. The following subsection (1.2.2) presents the other (types of) projects that had no explicit scientific aim, but rather an economic and/or dissemination goal. In the concluding section 1.3.1, we discuss how the results of the scientific projects address the STEVIN scientific priorities (Sect. 1.3.1) and how they are interrelated (Sect. 1.3.2). Finally, we explicate in section 1.4 the organisation of this volume.

In 1999, the Dutch Language Union, a binational intergovernmental organisation has created the HLT Platform as a means to start an exchange of plans and policy initiatives amongst government officials of Flanders and the Netherlands concerning human language technology for Dutch. During the past decade, the cooperation between these partners has intensified over time and culminated in the definition and execution of a joint R&D programme, called STEVIN. This paper summarises the past decade of (major) HLTD policy initiatives that led to the creation of the STEVIN in the Low Countries. It sketches the institutional framework in which the STEVIN-programme flourished. In addition, it provides the main conclusions of the evaluation the STEVIN programme. The external evaluators qualified the programme as successful.

Large speech corpora (LSC) constitute an indispensable resource for conducting research in speech processing and for developing real-life speech applications. In 2004 the Spoken Dutch Corpus (Corpus Gesproken Nederlands - CGN) became available, a corpus of standard Dutch as spoken by adult natives in the Netherlands and Flanders. CGN does not include speech of children, non-natives, elderly people and recordings of speech produced in human-machine interactions. Since such recordings would be extremely useful for conducting research and for developing HLT applications for these specific groups of speakers of Dutch, the JASMIN-CGN was started with the aim of extending CGN in three dimensions: age, mother tongue and interaction mode. First, by collecting a corpus of contemporary Dutch as spoken by children of different age groups, non-natives with different mother tongues and elderly people in the Netherlands and Flanders (JASMIN-CGN), we aimed at an extension along the age and mother tongue dimensions. In addition, we collected speech material in a communication setting that was not envisaged in CGN: human-machine interaction. One third of the data was collected in Flanders and two thirds in the Netherlands. The corpus has already been used in different ways within the STEVIN programme. In addition, it turned out to be useful for different lines of research. Since 2008 the JASMIN speech corpus has been available through the Dutch-Flemish HLT Agency. We hope that other researchers will make use of the data and the knowledge gathered in this project for further research and development.

Realistic phonetic transcriptions of names are crucial for many applications. A specific problem for the automatic recognition of names, is the existence of different pronunciations of the same name. These pronunciations often depend on the background (mother tongue) of the user. Typical examples are the pronunciation of foreign city names, foreign proper names, etc. The first goal of Autonomata was, therefore, to collect a large number of name pronunciations and to provide manually checked phonetic transcription of these name utterances. Such pronunciation data are needed both for training and evaluating name recognition systems. The second goal of the Autonomata project was to develop a tool that incorporates a state-of-the-art grapheme-to-phoneme convertor, as well as a dedicated phoneme-to-phoneme (p2p) post-processor which can automatically correct some of the mistakes which are being made by the standard g2p. In this contribution we will describe in more detail four resources that were developed in Autonomata and in Autonomata Too:1. The Autonomata Spoken Names Corpus (ASNC)2. The Autonomata transcription Toolbox3. The Autonomata P2P converters 4. The Autonomata TOO Spoken POI Corpus

This encompasses user help on diverse levels online, quick response to any questions by email, immediate handling of incidents and solving of problems, and last but not least, an infrastructure for user groups. The knowledge that the Praat program entails, is in this means passed on to many colleagues and students. Also, users have a way to relate to one another and share their insights with regard to the possibilities the Praat program offers.

The availability of a speech recognition system for Dutch is mentioned as one of the essential requirements for the language and speech technology community. Indeed, researchers now are faced with the problem that no good speech recognition tool is available for their purposes or existing tools lack functionality or flexibility. This project has two primary goals that are accomplished within a single software framework. The first goal is to develop a highly modular toolkit for research into speech recognition algorithms. It allows researchers to focus on one particular aspect of speech recognition technology without needing to worry about the details of the other components. The second goal is to provide a state-of-the art recogniser for Dutch with a simple interface, so that it can be used by non-specialists with a minimum of programming requirements. Next to speech recognition, the resulting software enables applications in related fields as well. Examples are linguistic and phonetic research where the software can be used to segment large speech databases or to provide high quality automatic transcriptions. We have chosen the existing ESAT recogniser, augmented with knowledge and code from the other partners in the project, as a starting point. This code base is transformed to meet the specified requirements. The transformation is accomplished by improving the software interfaces to make the software package more user friendly and adapted for usage in a large user community, and by providing adequate user and developer documentation written in English, so as to make it easily accessible to the international language and speech technology community as well.

Many natural language processing applications can benefit from the identification of coreference relations. For example, in information extraction and question answering, recall should in principle increase when the available information is expanded by linking expressions in a text that refer to the same discourse entity. Most current state-of-the-art systems for coreference resolution are based on supervised machine learning, and require (large) amounts of annotated data for training and testing. As these data were lacking for Dutch, the project had as its goal the construction of a coreference corpus for Dutch, and the development of an automatic coreference resolution system that used the corpus as training material. In this paper, we present the results of our annotation efforts, and the design of the automatic resolution system. Furthermore, we discuss various experiments that were carried out to determine the accuracy of the resolution system and the potential benefit of incorporating coreference resolution in tasks

Natural languages allow us to express essentially the same underlying meaning in a virtually unlimited number of alternative surface forms. In other words, there are often many similar ways to say the same thing. This characteristic poses a problem for natural language processing applications.

This chapter presents the Lassy Small and Lassy Large treebanks, as well as related tools and applications. Lassy Small is a corpus of written Dutch texts (1,000,000 words) which has been syntactically annotated with manual verification and correction. Lassy Large is a much larger corpus (over 500,000,000 words) which has been syntactically annotated fully automatically. In addition, various browse and search tools for syntactically annotated corpora have been developed and made available. Their potential for applications in corpus linguistics and information extraction has been illustrated and evaluated in a series of case studies.

One of the goals of the STEVIN programme is the realisation of a digital infrastructure that will enforce the position of the Dutch language in the modern information and communication technology.A semantic database makes it possible to go from words to concepts and consequently, to develop technologies that access and use knowledge rather than textual representations.