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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
  • 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 Note: this service has been withdrawn and the URLs and PID do not resolve anymore!
  • Nederlab, online laboratory for humanities research on Dutch text collections

    The Nederlab project aims to bring together all digitized texts relevant to Dutch national heritage, the history of Dutch language and culture (c. 800 - present) in one user-friendly and tool-enriched open access web interface, allowing scholars to simultaneously search and analyze data from texts spanning the full recorded history of the Netherlands, its language and culture. The project builds on various initiatives: for corpora Nederlab collaborates with the scientific libraries and institutions, for infrastructure with CLARIN (and CLARIAH), for tools with eHumanities programmes such as Catch, IMPACT and CLARIN (TICCL, frog). Nederlab will offer a large number of search options with which researchers can find the occurrence of a particular term in a particular corpus or subcorpus. It'll also offer visualization of search results through line graphs, bar graphs, circle graphs, or scatter graphs. Furthermore, this online lab will offer a large set of tools, like tokenization tools, tools for spelling normalization, PoS-tagging tools, lemmatization tools, a computational historical lexicon and indices. Also, the use of (semi-) automatic syntactic parsing, tools for text mining, data mining and sentiment mining, Named Entity Recognition tools, coreference resolution tools, plagiarism detection tools, paraphrase detection tools and cartographical tools is offered The first version of Nederlab was launched in early 2015, it’ll be expanded until the end of 2017. Nederlab is financed by NWO, KNAW, CLARIAH and CLARIN-NL.
    http://www.nederlab.nl/wp/?page_id=12
  • 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)
  • Metadata Editor, Browser and Organiser for IMDI and CMDI

    Arbil (Archive Builder) is a metadata editor, browser and organiser for metadata in IMDI and CMDI format. It is a Java desktop application that runs on most operating systems. Arbil can be used to create new metadata from scratch for resources on your local machine, or it can be used to download and modify metadata that are already in an archive. Arbil is a generic CMDI editor and therefore supports all CMDI profiles. It has a built-in file type verification tool that is configured to check files against the list of accepted file types for The Language Arhive, this can however be overruled for other archives.
  • GrETEL Search Engine for Querying Syntactic Constructions in Treebanks

    GrETEL is a query engine in which linguists can use a natural language example as a starting point for searching a treebank with limited knowledge about tree representations and formal query languages. Instead of a formal search instruction, it takes a natural language example as input. This provides a convenient way for novice and non-technical users to use treebanks with a limited knowledge of the underlying syntax and formal query languages. By allowing linguists to search for constructions similar to the example they provide, it aims to bridge the gap between descriptive-theoretical and computational linguistics. The example-based query procedure consists of several steps. In the first step the user enters an example of the construction he/she is interested in. In the second step the example is returned in the form of a matrix, in which the user specifies which aspects of this example are essential for the construction under investigation. The third step provides an overview of the search instruction, i.e. the subpart of the parse tree that contains the elements relevant for the construction under investigation. This query tree is automatically converted in an XPath query which can be used for the actual treebank search. This query can be edited if desired. In the fourth step the query is executed on the selected corpus. The matching constructions are presented to the user as a list of sentences, which can be downloaded. The user can also click on the sentences in order to visualize the results as syntax trees. GrETEL enables search in the LASSY-SMALL and the CGN (Spoken Dutch Corpus) Treebanks (1 million tokens each). GrETEL was created by CLARIN Dutch Language Union in Flanders in the context of the CLARIN-NL / CLARIN Flanders cooperation project.
    Liesbeth Augustinus, Vincent Vandeghinste, and Frank Van Eynde (2012). "Example-Based Treebank Querying" In: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC-2012). Istanbul, Turkey. pp. 3161-3167
    Augustinus, L, Vandeghinste, V, Schuurman, I and Van Eynde, F. 2017. GrETEL: A Tool for Example-Based Treebank Mining. In: Odijk, J and van Hessen, A. (eds.) CLARIN in the Low Countries, Pp. 269–280. London: Ubiquity Press. DOI: https://doi.org/10.5334/bbi.22. License: CC-BY 4.0
    http://gretel.ccl.kuleuven.be/project/publications.php
  • 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
  • ELAN Multimedia Annotator

    ELAN is a professional tool for the creation of complex annotations on video and audio resources. With ELAN a user can add an unlimited number of annotations to audio and/or video streams. An annotation can be a sentence, word or gloss, a comment, translation or a description of any feature observed in the media. Annotations can be created on multiple layers, called tiers. Tiers can be hierarchically interconnected. An annotation can either be time-aligned to the media or it can refer to other existing annotations. The textual content of annotations is always in Unicode and the transcription is stored in an XML format. ELAN provides several different views on the annotations, each view is connected and synchronized to the media playhead. Up to 4 video files can be associated with an annotation document. Each video can be integrated in the main document window or displayed in its own resizable window. ELAN delegates media playback to an existing media framework, like Windows Media Player, QuickTime or JMF (Java Media Framework). As a result a wide variety of audio and video formats is supported and high performance media playback can be achieved. ELAN is written in the Java programming language and the sources are available for non-commercial use. It runs on Windows, Mac OS X and Linux. ELAN has been functionally extended with the help of the following CLARIN-NL-funded projects: - ColTime: Collaboration on Time-Based Resources. - EXILSEA: Exploiting ISOcat's Language Sections in ELAN and ANNEX. - MultiCon: Multilayer Concordance Functions in ELAN and ANNEX. - SignLinC: Linking lexical databases and annotated corpora of signed languages. Over the years, many funders have contributed to the development of ELAN in several projects, such as the Volkswagen Foundation, the Royal Netherlands Academy of Arts and Sciences, the Berlin-Brandenburg Academy of Sciences and Humanities, the German Federal Ministry of Education and Research, the Max Planck Society and the ARC Centre of Excellence for the Dynamics of Language.
  • The Typological Database System (TDS)

    The Typological Database System (TDS) is a web-based service that provides integrated access to a collection of independently developed typological databases. Unified querying is supported with the help of an integrated ontology. The component databases of the TDS are cross-linguistic databases, developed for research in language typology and linguistics. Together they contain some 1200 different descriptive properties, with information about more than 1000 languages. (Because of the heterogeneous nature of the collection, most properties are only filled for a fraction of the languages). Most of the data is in the form of high-level "analytical" properties, but there are also a few collections of example sentences (with glosses) illustrating particular phenomena. Language typology, the study of the range of language variation and universals, is a data-intensive discipline that increasingly relies on electronic databases. Improved availability of the data collected in the TDS enhances its potential to support linguistic research. The TDS can be used to help answer questions such as "which languages have the basic word order Verb-Object-Subject", "what kind of phonological stress systems are common" "are languages with subject-verb agreement more likely to allow null subjects than languages without it" etc. The system is not an oracle: In all cases, only partial information is returned, as collected and deposited in the system by the creators of the component databases. But this information can be invaluable to other researchers, either as a complete answer to a specific question or as the starting point for further research. Given that the collected data represents linguistic analysis and often novel theoretical approaches, it is impossible to map it to a single "consensus" standard. While in some limited cases it is possible to completely reconcile data from different sources, the system places a premium on preserving the theoretical orientations and analyses of the component databases, which are presented side by side as alternative datasets in the same topical group. The TDS project was carried out by a research group of the Netherlands Graduate School of Linguistics (LOT), with members representing the University of Amsterdam, Leiden University, Radboud University Nijmegen, and Utrecht University. It was developed with support from NWO (Netherlands Organization for Scientific Research) grant 380-30-004 / INV-03-12 and from participating universities. The initial phase of the project was started in September 2000, and the project entered the implementation phase on 1 May 2004. Originally scheduled to run for three years, it was extended until 31 December 2007. The TDS server and data collections continued to be augmented until 2009. While the original TDS web server is still operational, web technologies evolve rapidly. The system had begun to show its age even before the end of the project in 2009, motivating migration of the data collection to an archival platform. But due to the complexity and diversity of the component databases, the data cannot be usefully navigated without specialized supporting software; useful archiving necessitates a software access point alongside the static data. Under the "TDS Curator" project, supported by a CLARIN-NL Call 1 grant, the TDS has migrated to a new platform, hosted by the Data Archiving and Networked Services (DANS), that conforms to CLARIN infrastructural requirements. Both versions of the system remain in operation.
    Windhouwer, M, Dimitriadis, A and Akerman, V. 2017. Curating the Typological Database System. In: Odijk, J and van Hessen, A. (eds.) CLARIN in the Low Countries, Pp. 123–132. London: Ubiquity Press. DOI: https://doi.org/10.5334/bbi.11. License: CC-BY 4.0
    A. Dimitriadis, M. Windhouwer, A. Saulwick, R. Goedemans, T. Bíró. How to integrate databases without starting a typology war: The Typological Database System. In S. Musgrave, M. Everaert and A. Dimitriadis (eds.), The use of databases in cross-linguistic research, Mouton de Gruyter, March 2009.
    M. Windhouwer, A. Dimitriadis. Sustainable operability: Keeping complex resources alive. In Proceedings of the LREC workshop on Sustainability of Language Resources and Tools for Natural Language Processing (SustainableNLP08 ), Marrakech, Morocco, May 31, 2008.
    A. Dimitriadis. Managing Differences: The TDS Approach. In Proceedings of the E-MELD Workshop on Toward the Interoperability of Language Resources (E-MELD 2007 ), Stanford, CA, July 13-15, 2007. Position paper.
    A. Dimitriadis, A. Saulwick, M. Windhouwer. Semantic relations in ontology mediated linguistic data integration. In Proceedings of the E-MELD Workshop on Morphosyntactic Annotation and Terminology: Linguistic Ontologies and Data Categories for Linguistic Resources (E-MELD 2005 ), Cambridge, Massachusetts, July 1-3, 2005.
    A. Saulwick, M. Windhouwer, A. Dimitriadis, R. Goedemans. Distributed tasking in ontology mediated integration of typological databases for linguistic research. In J. Castro and E. Teniente, Proceedings of the CAiSE'05 Workshops (International Workshop on Data Integration and the Semantic Web (DISWeb'05) in conjuction with CAiSE'05 ), Volume I, pp 303-317, Porto, Portugal, June 14, 2005.
    A. Dimitriadis, P. Monachesi. Integrating Different Data Types in a Typological Database System. In P. Austin, H. Dry and P. Wittenburg (eds.), Proceedings of the International Workshop on Resources and Tools in Field Linguistics, Las Palmas, Canary Islands, Spain, 2002.
    P. Monachesi, A. Dimitriadis, R. Goedemans, A. Mineur, M. Pinto. A Unified System for Accessing Typological Databases. In Proceedings of the Third International Conference on Language Resources and Evaluation (LREC 3), Las Palmas, Canary Islands, Spain, 2002.