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  • PICCL: Philosophical Integrator of Computational and Corpus Libraries

    PICCL is a set of workflows for corpus building through OCR, post-correction, modernization of historic language and Natural Language Processing. It combines Tesseract Optical Character Recognition, TICCL functionality and Frog functionality in a single pipeline. Tesseract offers Open Source software for optical character recognition. 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. Frog enriches textual documents with various linguistic annotations.
    Martin Reynaert, Maarten van Gompel, Ko van der Sloot and Antal van den Bosch. 2015. PICCL: Philosophical Integrator of Computational and Corpus Libraries. Proceedings of CLARIN Annual Conference 2015, pp. 75-79. Wrocław, Poland. http://www.nederlab.nl/cms/wp-content/uploads/2015/10/Reynaert_PICCL-Philosophical-Integrator-of-Computational-and-Corpus-Libraries.pdf
    PICCL

  • Frog: An advanced Natural Language Processing Suite for Dutch (Web Service and Application)

    Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch. It performs automatic linguistic enrichment such as part of speech tagging, lemmatisation, named entity recognition, shallow parsing, dependency parsing and morphological analysis. All NLP modules are based on TiMBL.
    Iris Hendrickx, Antal van den Bosch, Maarten van Gompel, Ko van der Sloot and Walter Daelemans. 2016.Frog: A Natural Language Processing Suite for Dutch. CLST Technical Report 16-02, pp 99-114. Nijmegen, the Netherlands. https://github.com/LanguageMachines/frog/blob/master/docs/frogmanual.pdf
    Van den Bosch, A., Busser, G.J., Daelemans, W., and Canisius, S. (2007). An efficient memory-based morphosyntactic tagger and parser for Dutch, In F. van Eynde, P. Dirix, I. Schuurman, and V. Vandeghinste (Eds.), Selected Papers of the 17th Computational Linguistics in the Netherlands Meeting, Leuven, Belgium, pp. 99-114. http://ilk.uvt.nl/downloads/pub/papers/tadpole-final.pdf
    Frog (plain text input)
    Frog (folia+xml input)

  • Ucto Tokeniser

    Ucto tokenizes text files: it separates words from punctuation, and splits sentences. This is one of the first tasks for almost any Natural Language Processing application. Ucto offers several other basic preprocessing steps such as changing case that you can all use to make your text suited for further processing such as indexing, part-of-speech tagging, or machine translation. The tokeniser engine is language independent. By supplying language-specific tokenisation rules in an external configuration file a tokeniser can be created for a specific language. Ucto comes with tokenization rules for English, Dutch, French, Italian, and Swedish; it is easily extendible to other languages. It recognizes dates, times, units, currencies, abbreviations. It recognizes paired quote spans, sentences, and paragraphs. It produces UTF8 encoding and NFC output normalization, optionally accepts other encodings as input. Optional conversion to all lowercase or uppercase. Ucto supports FoLiA XML.
    Ucto

  • Automatic Transcription of Oral History Interviews

    This webservice and web application uses automatic speech recognition to provide the transcriptions of recordings spoken in Dutch. You can upload and process only one file per project. For bulk processing and other questions, please contact Henk van den Heuvel at h.vandenheuvel@let.ru.nl.

  • Fast and easy development of pronunciation lexicons for names

    The AUTONOMATA transcription tool set consists of a transcription tool and learning tools, with which one can enrich word lists with precise information on the pronunciation. Thee uses a general grapheme-to-phoneme converter (the g2p-converter).
    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.
    De AUTONOMATA-transcriptietoolset bestaat uit een transcriptietool en learning tools, waarmee men woordenlijsten kan verrijken met nauwkeurige uitspraakinformatie. De tool maakt gebruik van een algemene grafeem-naar-foneemomzetter (de g2p-omzetter).

  • 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).

  • Frog: An advanced Natural Language Processing suite for Dutch

    Frog's current version will tokenize, tag, lemmatize, and morphologically segment word tokens in Dutch text files, will assign a dependency graph to each sentence, will identify the base phrase chunks in the sentence, and will attempt to find and label all named entities.
    Van den Bosch, A., Busser, G.J., Daelemans, W., and Canisius, S. (2007). An efficient memory-based morphosyntactic tagger and parser for Dutch, In F. van Eynde, P. Dirix, I. Schuurman, and V. Vandeghinste (Eds.), Selected Papers of the 17th Computational Linguistics in the Netherlands Meeting, Leuven, Belgium, pp. 99-114

  • 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 .

  • Dupira; the Dutch Parser for IR Applications

    Dupira is a rule-based parser, generated by means of the AGFL parser generator from the Dupira grammar, lexicon and fact tables. By means of transductions which are specified in the grammar (and can be modified), the parser transduces sentences to dependency graphs. Dupira was developed for practical applications in Information Retrieval and for Information Systems needing a Natural Language interface. Its intended users are computer scientists and computer professionals rather than linguists.