Result filters

Metadata provider

Language

  • French
...more

Resource type

Availability

Active filters:

  • Language: French
21 record(s) found

Search results

  • Universal Dependencies 2.15 models for UDPipe 2 (2024-11-21)

    Tokenizer, POS Tagger, Lemmatizer and Parser models for 147 treebanks of 78 languages of Universal Depenencies 2.15 Treebanks, created solely using UD 2.15 data (https://hdl.handle.net/11234/1-5787). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_215_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

  • Universal Dependencies 2.12 models for UDPipe 2 (2023-07-17)

    Tokenizer, POS Tagger, Lemmatizer and Parser models for 131 treebanks of 72 languages of Universal Depenencies 2.12 Treebanks, created solely using UD 2.12 data (https://hdl.handle.net/11234/1-5150). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_212_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

  • CUBBITT Translation Models (en-fr) (v1.0)

    CUBBITT En-Fr translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). Models are compatible with Tensor2tensor version 1.6.6. For details about the model training (data, model hyper-parameters), please contact the archive maintainer. Evaluation on newstest2014 (BLEU): en->fr: 38.2 fr->en: 36.7 (Evaluated using multeval: https://github.com/jhclark/multeval)

  • iDAI.vocab FR

    The German Archaeological Institute is a scientific institution of the confederation in the business area of the Department of Foreign Affairs.

  • LINDAT Translation

    The input file size is limited to 100kB. Translates from->to: Czech->English, Hindi, French, Russian, German English->Russsian, German, Czech, Hindi, French Russian->German, French, Czech, Hindi, English German->Russian, Hindi, Czech, English, French French->Russian, German, Czech, English, Hindi

  • WebLicht POSTags Lemmas FR

    WebLicht Easy Chain for POS Tagging and Lemmatization (French). The pipeline makes use of WebLicht's TCF converter, the IMS tokenizer, and the IMS TreeTagger. WebLicht's Tundra can be used to visualize the result.

  • UDPipe

    UDPipe is an trainable pipeline for tokenization, tagging, lemmatization and dependency parsing of CoNLL-U files. UDPipe is language-agnostic and can be trained given only annotated data in CoNLL-U format. Trained models are provided for nearly all UD treebanks.

  • 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

  • Usage

    The system here allows you to convert your book pages' images into editable text, presented in a particular text format called XML (eXtended Markup Language) of a particular type called Text-Encoding Initiative or TEI XML. This particular format was developed specifically for being able to mark-up or annotate the text you want to work on, i.e. to add all manner of further information to the actual text, e.g. to build a critical edition of it, which is most likely exactly what you want to do with your author's work.
    Betti, A, Reynaert, M and van den Berg, H. 2017. @PhilosTEI: Building Corpora for Philosophers. In: Odijk, J and van Hessen, A. (eds.) CLARIN in the Low Countries, Pp. 379–392. London: Ubiquity Press. DOI: https://doi.org/10.5334/bbi.32. License: CC-BY 4.0