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  • TTNWW - TST Tools for the Dutch Language as Web services in a Workflow

    TTNWW integrates and makes available existing Language Technology (LT) software components for the Dutch language that have been developed in the STEVIN and CGN projects. The LT components (for text and speech) are made available as web-services in a simplified workflow system that enables researchers without much technical background to use standard LT workflow recipes. The web services are available in two separate domains: "Text" and "Speech" processing. For "Text", workflows for the following functionality is offered by TTNWW: - Orthographic Normalisation using TICCLops (version CLARIN-NL 1.0); - Part of Speech Tagging, Lemmatisation, Chunking, limited Multiword Unit Recognition, and Grammatical Relation Assignment by Frog (Version 012.012); - Syntactic Parsing (including grammatical relation assignment, limited named entity recognition, and limited multiword unit recognition) by the Alpino Parser (version 1.3); - Semantic Annotation; - Named Entity Recognition; - Co-reference Assignment. For "Speech", the following workflows are offered: - Automatic Transcription of speech files using a Netherlands Dutch acoustic model; - Automatic Transcription of speech files using a Flemish Dutch acoustic model; - Conversion of the input speech file to the required sampling rate, followed by automatic transcription. The TTNWW services have been created in a Dutch and Flemish collaboration project building on the results of past Dutch and Flemish projects. The web services are partly deployed in the SURF-SARA BiG-Grid cloud or at CLARIN centres in the Netherlands and at CLARIN VL University partners. The architecture of the TTNWW portal consists out of several components and follows the principles of Service Oriented Architecture (SOA). The TTNWW GUI front-end is a Flex module that communicates with the TTNWW web-application which keeps track of the different sessions and knows which LT recipes are available. TTNWW communicates assigments (workflow specifications) to the WorkflowService that evaluates the requested workflow and requests the DeploymentSevice to start the required LT web-services. After initialization of the LT web-services, the workflow specification is sent to the Taverna Server, that takes further care of the workflow. To facilitate the process of wrapping applications that were originally designed as standalone applications into web services, the CLAM (Computational Linguistics Application Mediator) wrapper software allows for easy and transparent transformation of applications into RESTful web services. The CLAM software has extensively been used in the TTNWW project for both text and speech processing tools. With the exception of Alpino and MBSRL all web services work operate on CLAM wrappers. Given the number of web services involved in the TTNWW project and possibilities offered by the cloud environment the preferred method of delivering the web service installations was delivery of complete virtual machine images by the LT providers. These could be directly uploaded into the cloud environment and thus relieving the CLARIN centres nd LT providers from the original foreseen task of running the webservices themselves. A potential advantage of this method, that has not been exploited in the project yet, is that these images may be also be delivered directly to the end user so these can be run in a local configuration using virtualization software such as VMWare of VirtualBox. The workflow engine used in the project was Taverna. But build on top of this was a a number of selectable task recipes, following a task oriented approach in line with the premises that users with no or little technical expertise should be able to use the system. In this context, tasks are understood in terms of end results of processes such as semantic role labelling, pos tagging or syntactic analysis and ready-made workflows are constructed that can be readily used by the end user.
    Kemps-Snijders, M, Schuurman, I, Daelemans, W, Demuynck, K, Desplanques, B, Hoste, V, Huijbregts, M, Martens, J-P, Paulussen, H, Pelemans, J, Reynaert, M, Vandeghinste, V, van den Bosch, A, van denHeuvel, H, van Gompel, M, van Noord, G and Wambacq, P. 2017. TTNWW to the Rescue: No Need to Know How to Handle Tools and Resources. In: Odijk, J and van Hessen, A. (eds.) CLARIN in the Low Countries, Pp. 83–93. London: Ubiquity Press. DOI: https://doi.org/10.5334/bbi.7. License: CC-BY 4.0

  • RemBench - a Digital Workbench for Rembrandt Research

    RemBench enables one to search and browse for works of art, artists, primary sources and library sources related to Rembrandt, using faceted search by location, author/artist name, author/artist type, and date range, and/or by both exact and fuzzy keyword search. It offers both a web application and a RESTful web service. RemBench combines the content of four different databases behind one search interface: RKDartists and RKDimages, two databases maintained by the Netherlands Institute for Art History (RKD); RemDoc, a collection of original documents related to Rembrandt van Rijn from the period between 1475 to circa 1750; RUQuest, a library system that provides access to full text articles, as well as the complete collection of (e-)books and journals from the Radboud University Library Catalogue. RemBench does not influence the content of these databases.
    Verberne, S, van Leeuwen, R, Gerritsen, G and Boves, L. 2017. RemBench: A Digital Workbench for Rembrandt Research. In: Odijk, J and van Hessen, A. (eds.) CLARIN in the Low Countries, Pp. 337–350. London: Ubiquity Press. DOI: https://doi.org/10.5334/bbi.28. License: CC-BY 4.0

  • Evaluating Repetitions, or how to Improve your Multilingual ASR System by doing Nothing

    A demo of a speech recognizer for POIs (Points of Interest). This demo recognizes stay-over addresses and eateries in some big cities (inter alia Amsterdam, Antwerpen, Gent, Rotterdam).
    This STEVIN project is about the investigation of new pronunciation modeling technologies that can improve the automatic recognition of spoken names in the context of a POI (Point-of-Interest) information providing business service. Collaboration with RU (Nijmegen), UiL (Utrecht), Nuance and TeleAtlas.
    Een demo van een spraakherkenner voor POIs (Points of Interest). Deze demo herkent overnachtingsadressen en eetgelegenheden in enkele grote steden (o.a. Amsterdam, Antwerpen, Gent, Rotterdam).

  • MIGMAP: Detailed interactive mapping of migration in The Netherlands in the 20th century.

    MIGMAP is a web application that can show migration flow between Dutch municipalities. The user first chooses generation (forward or backward in time) and gender, while subsequently the migration map of The Netherlands related to an interactively pointed municipality (or other aggregation unit) is shown. The data underlying the migration maps originate from the first name selection from the Civil Registration, acquired by Utrecht University and the Meertens Institute in 2006. These concern 16 million records from persons with Dutch citizenship, alive in 2006, and in addition 6 million persons deceased before 2006, but mentioned in other records – mainly as parents. The records include identifiers by which family relations can be reconstructed. After considerable efforts in data clearing and reconstruction of older generations, the data provide an almost complete overview of the Dutch population, born after 1930, and a fairly good sample from the period 1880-1930 (>25%). The user will be given options to choose generation (places of birth of the current population, their parents, grandparents grand-grandparents, or starting with the persons born between 1880-1900: the current places of residence of their children, grandchildren), and gender. Each map will be made available as a .csv record with municipality number and percentage as fields, and thus can be used by users in correlation studies with other variables. Utrecht University and the Meertens Institute have the signed permission of the "Basisadministratie voor Persoonsgegevens en Reisdocumenten, The Hague" to use the data for scientific purposes. The migration maps present the data in an aggregated way, and do not violate privacy requirements (no individual can possibly be identified from the maps). However, the underlying data containing information about individual persons and their family relations cannot be made available for reasons of privacy.
    Bloothooft, G, Onland, D and Kunst, J.P. 2017. Mapping Migration across Generations. In: Odijk, J and van Hessen, A. (eds.) CLARIN in the Low Countries, Pp. 351–360. London: Ubiquity Press. DOI: https://doi.org/10.5334/bbi.29. License: CC-BY 4.0
    Ekamper, P. en Bloothooft, G. (2013), "Weg van je wortels. De afstand tussen overgrootouders en achterkleinkinderen", DEMOS 29, 2, p8.

  • Namescape Barcode Browser

    Searching and visualizing Named Entities in modern Dutch novels. The named entity (NE) tagging and resolution in NameScape enables quantitative and repeatable research where previously only guesswork and anecdotal evidence was feasible. The visualisation module enables researchers with a less technical background to draw conclusions about functions of names in literary work and help them to explore the material in search of more interesting questions (and answers). Users from other communities (sociolinguistics, sentiment analysis, …) also benefit from the NE tagged data, especially since the NE recognizer is available as a web service, enabling researchers to annotate their own research data. Datasets in NameScape (total of 1.129 books): Corpus Sanders: A corpus of 582 Dutch novels written and published between 1970 and 2009 will. Corpus Huygens: Consists of 22 novels manually tagged with detailed named entity information. IPR for this corpus do not allow distribution. Corpus eBooks: Consists of 7000+ Dutch eBooks tagged automatically with basic NER features and person name Part information. IPR for this corpus do not allow distribution. Corpus SoNaR Books: 105 Dutch books; NE tagged. Corpus Gutenberg Dutch: Consists of 530 NE tagged TEI files converted from the Epub versions of the corresponding Gutenberg documents. Recent research has conclusively proven names in literary works can only be put fully into perspective when studied in a wider context (landscape) of names either in the same text or in related material (the onymic landscape or “namescape”). Research on large corpora is needed to gain a better understanding of e.g. what is characteristic for a certain period, genre, author or cultural region. The data necessary for research on this scale simply does not exist yet. NameScape aims to fill the need by providing a substantial amount of literary works annotated with a rich tag set, thereby enabling researchers to perform their research in more depth than previously possible. Several exploratory visualization tools help the scholar to answer old questions and uncover many more new ones, which can be addressed using the demonstrator.
    de Does, J, Depuydt, K, van Dalen-Oskam, K and Marx, M. 2017. Namescape: Named Entity Recognition from a Literary Perspective. In: Odijk, J and van Hessen, A. (eds.) CLARIN in the Low Countries, Pp. 361–370. London: Ubiquity Press. DOI: https://doi.org/10.5334/bbi.30. License: CC-BY 4.0
    Karina van Dalen-Oskam (2013), Nordic Noir: a background check on Inspector Van Veeteren, 31 May 2012, http://blog.namescape.nl/?p=47

  • TiCClops: Text-Induced Corpus Clean-up online processing system

    TICCL (Text Induced Corpus Clean-up) is a system that is designed to search a corpus for all existing variants of (potentially) all words occurring in the corpus. This corpus can be one text, or several, in one or more directories, located on one or more machines. TICCL creates word frequency lists, listing for each word type how often the word occurs in the corpus. These frequencies of the normalized word forms are the sum of the frequencies of the actual word forms found in the corpus. TICCL is a system that is intended to detect and correct typographical errors (misprints) and OCR errors (optical character recognition) in texts. When books or other texts are scanned from paper by a machine, that then turns these scans, i.e. images, into digital text files, errors occur. For instance, the letter combination `in' can be read as `m', and so the word `regeering' is incorrectly reproduced as `regeermg'. TICCL can be used to detect these errors and to suggest a correct form. Text-Induced Corpus Clean-up (TICCL) was developed first as a prototype at the request of the Koninklijke Bibliotheek - The Hague (KB) and reworked into a production tool according to KB specifications (currently at production version 2.0) mainly during the second half of 2008. It is a fully functional environment for processing possibly very large corpora in order to largely remove the undesirable lexical variation in them. It has provisions for various input and output formats, is flexible and robust and has very high recall and acceptable precision. As a spelling variation detection system it is to the developer’s knowledge unique in making principled use of the input text as possible source for target output canonical forms. As such it is far less domain-sensitive than other approaches: the domain is largely covered by the input text collection. TICCL comes in two variants: one with a classic CLAM web application interface, and one with the PhilosTEI interface.
    Reynaert, M. (2008). All, and only, the errors: More complete and consistent spelling and OCR-error correction evaluation. In: Proceedings of the Sixth International Language Resources and Evaluation (LREC’08), Marrakech, Morocco.
    Reynaert, M. (2010). Character confusion versus focus word-based correction of spelling and ocr variants in corpora. International Journal on Document Analysis and Recognition, pp 1-15, URL http://dx.doi.org/10.1007/s10032-010-0133-5

  • user documentation

    Search Application into the collection of all 13th century texts that served as source material for the Early Middle Dutch Dictionary. The source files are available upon request.
    Zoekapplicatie op de verzameling van alle 13e-eeuwse teksten die als bronnenmateriaal hebben gediend voor het Vroegmiddelnederlands Woordenboek. De bronbestanden van het corpus zijn op aanvraag beschikbaar

  • DuELME: Search interface to the Dutch Electronic Lexicon of Multiword Expressions

    The DuELME search interface provides access to the DUELME electronic lexicon, which contains more than 5,000 Dutch multiword expressions (MWEs). MWEs with the same syntactic pattern are grouped in the same equivalence class. The search interface enables users to search for MWEs on the basis of a range of syntactic and semantic criteria, among them expression, pattern id, written form, type, conjugation, polarity, parameters, form, etc. Extensive documentation on the structure of the database is available. DuELME (Dutch Electronic Lexicon of Multiword Expressions) is one of the results of the project Identification and Representation of Multiword Expressions (IRME). The lexical descriptions boast to be highly theory- and implementation-neutral. The DUELME LMF lexicon is suitable for theoretical research on multiword expressions as for use in NLP systems. The DuELME-LMF project has been carried out within the CLARIN-NL programme.
    Grégoire, N. (2009), Untangling Multiword Expressions. A study on the representation and variation of Dutch multiword expressions, PhD thesis, University of Utrecht.

  • Alpino: a dependency parser for Dutch

    Alpino is a dependency parser for Dutch, developed in the context of the PIONIER Project Algorithms for Linguistic Processing.
    Bouma, G., van Noord, G. J. M. and Malouf, R. 2001.Alpino: Wide-coverage computational analysis of Dutch. in Daelemans, W., Simaan, K., Veenstra, J. and Zavrel, J. (eds.). Computational Linguistics in the Netherlands 2000. Amsterdam: Rodopi, p. 45-59 15 p. (LANGUAGE AND COMPUTERS : STUDIES IN PRACTICAL LINGUISTICS)
    Robert Malouf and Gertjan van Noord. Wide Coverage Parsing with Stochastic Attribute Value Grammars. In: IJCNLP-04 Workshop Beyond Shallow Analyses - Formalisms and statistical modeling for deep analyses.
    Leonoor van der Beek, Gosse Bouma, and Gertjan van Noord. Een brede computationele grammatica voor het Nederlands. 2002. Nederlandse Taalkunde. https://www.let.rug.nl/~vannoord/papers/taalkunde.pdf .