Result filters

Metadata provider

Language

Resource type

  • Web service

Availability

  • Public

Active filters:

  • Availability: Public
  • Resource type: Web service
7 record(s) found

Search results

  • 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

  • Automatic Annotation of Multi-modal Language Resources

    The AAM-LR project provides a web service that helps field researchers to annotate audio- and video-recordings. At the top level the service marks the time intervals at which specific persons in the recording are speaking. In addition, the service provides a global phonetic annotation, using language independent phone models and phonetic features. Speech is separated from speaker noises such as laughing. Note: this service has been withdrawn and the URLs and PID do not resolve anymore!

  • 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

  • CLARIN Vocabulary Service

    The CLARIN Vocabulary Service is a running instance of the OpenSKOS exchange and publication platform for SKOS vocabularies. OpenSKOS offers several ways to publish SKOS vocabularies (upload SKOS file, harvest from another OpenSKOS instance with OAI-PMH, construct using the RESTful API) and to use vocabularies (search and autocomplete using the API, harvest using OAI-PMH, inspect in the interactive Editor or consult as Linked Data). This CLARIN OpenSKOS instance is hosted by the Meertens Institute. Contents This OpenSKOS instance currently publishes SKOS versions of three vocabularies: - ISO-639-3 language codes, as published by SIL. - Closed and simple Data Categories from the ISOcat metadata profile. - A manually constructed and curated list of Organizations, based on the CLARIN VLO. .
    Brugman, H. 2017. CLAVAS: A CLARIN Vocabulary and Alignment Service. In: Odijk J. & van Hessen A, CLARIN in the Low Countries, ch 5, pp 61-69. London: Ubiquity Press. DOI: https://doi.org/10.5334/bbi.5

  • CLARIN Concept Registry

    The CCR is a concept registry according to the W3C SKOS recommendation. It was chosen by CLARIN to serve as a semantic registry to overcome semantic interoperability issues with CMDI metadata and different annotation tag sets used for linguistic annotation. The CCR is part of the CMDI metadata infrastructure. The W3C SKOS recommendation, and the OpenSKOS implementation thereof, provides the means for ‘data-sharing, bridging several different fields of knowledge, technology and practice’. According to this model, each concept is assigned a unique administrative identifier, together with information on the status or decision-making process associated with the concept. In addition, concept specifications in the CCR contain linguistic descriptions, such as definitions and examples, and can be associated with a variety of labels. .

  • CMDI to RDF conversion

    There is growing amount of on-line information available in RDF format as Linked Open Data (LOD) and a strong community very actively promotes its use. The publication of information as LOD is also considered an important signal that the publisher is actively searching for information sharing with a world full of new potential users. Added advantages of LOD, when well used, are the explicit semantics and high interoperability. But the problematic modelling by non-expert users offsets these advantages, which is a reason why modelling systems as CMDI are used. The CMDI2RDF project aims to bring the LOD advantages to the CMDI world and make the huge store of CMDI information available to new groups of users and at the same time offer CLARIN a powerful tool to experiment with new metadata discovery possibilities. The CMD2RDFservice was created to allow connection with the growing LOD world, and facilitate experiments within CLARIN merging CMDI with other, RDF based, information sources. One of the promises of LOD is the ease to link data sets together and answer queries based on this ‘cloud’ of LOD datasets. Thus in the enrichment and use cases part of the project we looked at other datasets to link to the CLARIN joint metadata domain. We used the WALS N3 RDF dump for one of the use cases. Although it is in the end relatively easy to go from a specific typological feature to the CMD records via a shared URI, it still showcased a weakness of the Linked Data approach. One has to carefully inspect the property paths involved. And in this case the path was broken as there was no clear way to go from the WALS feature data to the WALS language info except for extracting the WALS language code from the feature URI pattern and insert it the language URI pattern. This showcases that although the big LOD cloud shows potential for knowledge discovery by crossing dataset boundaries, design decisions in the individual datasets can still hamper algorithms and manual inspection is needed. The CMD2RDF service was developed at the TLA/MPI for Psycholinguistics and DANS and later moved to Meertens Institute where the expertise remains.