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Scientific research within the humanities is different from what it was a few decades ago. Forinstance, new sources of information, such as digital grammars, lexical databases and largecorpora of real-language data offer new opportunities for linguistics. The Taalportaal gram-matical database, with its links to other linguistic resources via the CLARIN infrastructure,is a prime example of a new type of tool for linguistic research.

We present a method for automatically converting the Dutch Lassy Small treebank, a phrasal dependency treebank, to UD. All of the information required to produce accurate UD annotation appears to be available in the underlying annotation. However, we also note that the close connection between POS-tags and dependency labels that is present in UD is missing in the Lassy treebanks. As a consequence, annotation decisions in the Dutch data for such phenomena as nominalization and clausal complements of prepositions seem to differ to some extent from comparable data in English and German. Because the conversion is automatic, we can now also compare three state-of-theart dependency parsers trained on UD Lassy Small with Alpino, a hybrid Dutch parser which produces output that is compatible with the original Lassy annotations.

This paper deals with a dramatic change in the presentation of linguistic examples in the linguistics literature of the twentieth century, a change coinciding (not accidentally) with the introduction of transformational generative grammar (TGG) in the 1950s. Our investigation of this change and the circumstances that gave rise to it leads us to reconsider the question of continuity and discontinuity in the history of linguistics in this crucial period, focusing on the question of the audience to which the earliest publications in TGG were directed. We argue that this was an audience of nonlinguists (information theorists and mathematical logicians) and that transformations were introduced by Chomsky, the founder of TGG, as a way to preserve, within the new paradigm of formal grammar theory, established insights of a purely linguistic nature.

The Syntactic Atlas of Dutch Dialects (SAND) is a database of syntactic features observed in the language spoken by people from different dialect regions in The Netherlands and Flanders. We would like to know how specific syntactic features are for the different dialects. For this purpose we try to generate dialect boundaries from the syntactic data only. We show that a plausible binary division of the dialect can successfully be derived but that is more difficult to divide the data in three or more regions. We build on earlier work by Nerbonne, Heeringa en Kleiweg (1999), who performed this task for phonetic data, and on work by Spruit (2008), who also attempted to identify dialect regions from syntactic data.

Although sentence final verbal clusters in dialects of Dutch demonstrate a large amount of variation in the order of verbs, we argue that this is only apparently so. We take each dialect to allow just one order of verbs in three-verb clusters with a past participle. In the north of the Dutch language area, the order is descending (V3-V2-V1) and the rest of the dialects show an ascending order (V1-V2-V3). The large amount of apparent counterexamples will be explained by independently motivated, interfering properties. First, participles might be V-type or A-type. Only V-type participles occur in V-positions in the verbal cluster. Secondly, non-verbal elements (such as A-type participles) may interrupt a verbal cluster. We will show that the distribution of the different orders in dialects of Dutch strongly supports such a restrictive approach. We thus take this to be an argument that a structural approach to dialectology is required to gain insight in the properties of the formation of verbal clusters in Dutch.

Data, methods and resultsIn this study, we investigate crosslinguistic patterns in the alternation between UM, a hesitation marker consisting of a neutral vowel followed by a final labial nasal, and UH, a hesitation marker consisting of a neutral vowel in an open syllable. Based on a quantitative analysis of a range of spoken and written corpora, we identify clear and consistent patterns of change in the use of these forms in various Germanic languages (English, Dutch, German, Norwegian, Danish, Faroese) and dialects (American English, British English), with the use of UM increasing over time relative to the use of UH. We also find that this pattern of change is generally led by women and more educated speakers. Finally, we propose a series of possible explanations for this surprising change in hesitation marker usage that is currently taking place across Germanic languages.

Available on-line at http://www.aclweb.org/anthology/E/E14/#4000; International audience

Modern computer-algebra programs are able to solve a wide range of mathematical calculations. However, they are not able to understand and solve math text problems in which the equation is described in terms of natural language instead of mathematical formulas. Interestingly, there are only few known approaches to solve math word problems algorithmically and most of employ models based on frames. To overcome problems with existing models, we propose a model based on augmented semantic networks to represent the mathematical structure behind word problems. This model is implemented in our Solver for Mathematical Text Problems (SoMaTePs) [1], where the math problem is extracted via natural language processing, transformed in mathematical equations and solved by a state-of-the-art computer-algebra program. SoMaTePs is able to understand and solve mathematical text problems from German primary school books and could be extended to other languages by exchanging the language model in the natural language processing module.

In the course of the IMIX project a system was developed to demonstrate how the research performed in the various subprojects could contribute to the development of practical multimodal question answering dialog systems. This chapter describes the IMIX Demonstrator, an information search assistant for the medical domain. The functionalities and the architecture of the system are described, as well as its role in the IMIX project.

In this chapter, we describe our efforts in text-to-text generation within the IMOGEN project. In particular, we describe two focus areas of research to improve the quality of the answer: (a) graph-based content selection to improve the answer in terms of usefulness, and (b) sentence fusion to improve the answer in terms of formulation. We use sentence fusion to join together multiple sentences in order to eliminate overlapping parts, thereby reducing redundancy. The results of this work have been applied in the IMIX system. This system uses a question answering system to pinpoint fragments of text which are relevant to the information need expressed by the user. A content selection system then uses these fragments as entry points in the text to formulate a more complete answer. Sentence fusion is applied to manipulate the result in order to increase the fluency of the text.

This chapter describes the Vidiam project, which covered the development of a dialogue management system for multimodal question answering (QA) dialogues, as carried out in the IMIX project. The approach followed was datadriven, i.e., corpus-based. Since research in QA dialogue of multimodal information retrieval is still new, no suitable corpora were available to base a system on. This chapter reports on the collection and analysis of three QA dialogue corpora, involving textual follow-up utterances, multimodal follow-up questions, and speech dialogues. Based on the data, a dialogue act typology was created, which helps translate user utterances to practical interactive QA strategies.

Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. NEALT Proceedings Series, Vol. 9 (2010), 139-150. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15891 .

We describe our system for the monolingual Dutch and multilingual English to Dutch QA tasks. First, we present a brief overview of our QA-system, which makes heavy use of syntactic information. Next, we describe the modules that were developed especially for CLEF 2007, i.e. preprocessing of Wikipedia, inclusion of query expansion in IR, anaphora resolution in follow-up questions, and a question classification module for the multilingual task. We achieved 25.5% accuracy for the Dutch monolingual task, and 13.5% accuracy for the multilingual task.