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  • 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.
  • Web-based Annotation Explorer

    Annex (Annotation Explorer) is a web-based tool for exploring and viewing annotated multimedia recordings in an archive. ANNEX can play audio and video files in a web browser along with annotations in a variety of formats: ELAN (EAF), Shoebox/Toolbox text, CHAT (CHILDES annotation format), Plain text, CSV, PDF, SubRip, Praat TextGrid, HTML and XML. Annex will visualise the annotations in synchrony with the media files as long as time-alignment information is available. If no time-alignment information is available, a default segment duration is assumed. Annex has a graphical interface that resembles the interface of the ELAN annotation tool to some extent, with a number of different view modes such as subtitle view, timeline view and grid view. Annex runs in any modern web browser with the Adobe Flash plugin (> version 10) installed. ANNEX 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 ANNEX 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 and the the Max Planck Society.
  • Saper

    Shallow semantic parser for polish texts processing. Contains word sense disambiguation, mapping go SUMO concepts and semantic role labelling.
  • The CLASSLA-Stanza model for semantic role labeling of standard Slovenian 2.0

    The model for semantic role labeling of standard Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SUK training corpus (http://hdl.handle.net/11356/1747) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204) extended with the MaCoCu-sl Slovenian web corpus (http://hdl.handle.net/11356/1517). The estimated F1 of the semantic role annotations is ~76.24. The difference to the previous version of the model is that the model was trained using the SUK training corpus and the updated word embeddings.
  • The CLASSLA-StanfordNLP model for named entity recognition of non-standard Croatian 1.0

    This model for named entity recognition of non-standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the hr500k training corpus (http://hdl.handle.net/11356/1183), the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241) and the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240), using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205). The training corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed.
  • The CLASSLA-Stanza model for lemmatisation of spoken Slovenian 2.2

    This model for lemmatisation of spoken Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SST treebank of spoken Slovenian (https://github.com/UniversalDependencies/UD_Slovenian-SST) combined with the SUK training corpus (http://hdl.handle.net/11356/1959) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1791) that were expanded with the MaCoCu-sl Slovene web corpus (http://hdl.handle.net/11356/1517). The estimated F1 of the lemma annotations is ~99.23.
  • The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Croatian

    The model for morphosyntactic annotation of standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the hr500k training corpus (http://hdl.handle.net/11356/1183) and using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~94.1.
  • SuperMatrix

    SuperMatrix is a system to support automatic extraction of semantic relations, based on the analysis of large text corpora. System was developed as a tool for expansion of Polish wordnet (Słowosieć).Expansion consist of two steps: system suggests a potential links between lexical units. Linguist verify these suggestions and decide which form will go to wordnet. This speeded up the work and preserve the integrity of data entry.
  • The CLASSLA-StanfordNLP model for named entity recognition of non-standard Slovenian 1.0

    This model for named entity recognition of non-standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1210) and the Janes-Tag training corpus (http://hdl.handle.net/11356/1238), using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204). The training corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed.