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  • Tool task: Dependency parsing
  • Keywords: dependency parsing
12 record(s) found

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  • Integrated Parser

    Integrated parser is an application that combines and normalizes outputs of several parsers for Polish. It is based on ENIAM processing stream extended with Polish Dependency Parser, Świgra and POLFIE. Particular parsers may turned on and off according to the user requirements.

  • DG-POLFIE: POLFIE and Malt-based syntactic parser

    DG-POLFIE is a prototypical parser that tries to merge parse fragments generated by POLFIE using Polish Dependency Parser DG-POLFIE aims to improve the coverage of the POLFIE parser (i.e. the percentage of sentences with at least one analysis). In order to increase the number of Polish sentences and constructions that could be parsed with the POLFIE-based parser, DG-POLFIE defines some rules that use depenency structure to build full parse from the FRAGMENTS provided by POLFIE.

  • Frog

    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.

  • Alpino-Webservice

    Alpino is a dependency parser for Dutch, developed in the context of the PIONIER Project Algorithms for Linguistic Processing, developed by Gertjan van Noord at the University of Groningen. This is the webservice for it. You can upload either tokenised or untokenised files (which will be automatically tokenised for you using ucto), the output will consist of a zip file containing XML files, one for each sentence in the input document.

  • Trankit model for SST 2.15 1.1

    This is a retrained Slovenian model for the Trankit v1.1.1 library for multilingual natural language processing (https://pypi.org/project/trankit/), trained on the SST treebank of spoken Slovenian (UD v2.15, https://github.com/UniversalDependencies/UD_Slovenian-SST/tree/r2.15) featuring transcriptions of spontaneous speech in various everyday settings. It is able to predict sentence segmentation, tokenization, lemmatization, language-specific morphological annotation (MULTEXT-East morphosyntactic tags), as well as universal part-of-speech tagging, morphological feature prediction, and dependency parses in accordance with the Universal Dependencies annotation scheme (https://universaldependencies.org/). Please note this model has been published for archiving purposes only. For production use, we recommend using the state-of-the art Trankit model available here: http://hdl.handle.net/11356/1965 (v1.2 or newest). The latter was trained on both spoken (SST) and written (SSJ) data, and demonstrates a significantly higher performance to the model featured in this submission. In comparison with version 1.0, this model was trained on a new train-dev-test split of the SST treebank introduced in release UD v2.15.

  • The Trankit model for linguistic processing of spoken and written Slovenian 1.1

    This is a retrained Slovenian model for the Trankit v1.1.1 library for multilingual natural language processing (https://pypi.org/project/trankit/), trained on the concatenation of the SSJ UD treebank of written Slovenian (featuring fiction, non-fiction, periodicals and Wikipedia texts) and the SST UD treebank of spoken Slovenian (featuring transcriptions of spontaneous speech in various settings). It is able to predict sentence segmentation, tokenization, lemmatization, language-specific morphological annotation (MULTEXT-East morphosyntactic tags), as well as universal part-of-speech tagging, morphological features, and dependency parses in accordance with the Universal Dependencies annotation scheme (https://universaldependencies.org/). In comparison to its counterpart models trained on SSJ (http://hdl.handle.net/11356/1963) or SST datasets only, this model yields a significantly better performance on spoken transcripts and an almost identical state-of-the-art performance on written texts. The model can therefore be recommended as the default, 'universal' Trankit model for processing Slovenian, regardless of the data type. To utilize this model, please follow the instructions provided in our github repository (https://github.com/clarinsi/trankit-train) or refer to the Trankit documentation (https://trankit.readthedocs.io/en/latest/training.html#loading). This ZIP file contains models for both xlm-roberta-large (which delivers better performance but requires more hardware resources) and xlm-roberta-base. In comparison to the previous version, this version was trained on a newer, slightly improved version of the SSJ UD treebank (UD v2.14, https://github.com/UniversalDependencies/UD_Slovenian-SSJ/tree/r2.14) and a substantially extended and improved version of the SST UD treebank (UD v2.15, https://github.com/UniversalDependencies/UD_Slovenian-SST/tree/dev), thus producing significantly better results for spoken data.

  • Trankit model for SST 2.15

    This is a retrained Slovenian model for the Trankit v1.1.1 library for multilingual natural language processing (https://pypi.org/project/trankit/), trained on the SST treebank of spoken Slovenian (UD v2.15, https://github.com/UniversalDependencies/UD_Slovenian-SST/tree/dev) featuring transcriptions of spontaneous speech in various everyday settings. It is able to predict sentence segmentation, tokenization, lemmatization, language-specific morphological annotation (MULTEXT-East morphosyntactic tags), as well as universal part-of-speech tagging, morphological feature prediction, and dependency parses in accordance with the Universal Dependencies annotation scheme (https://universaldependencies.org/). Please note this model has been published for archiving purposes only. For production use, we recommend using the state-of-the art Trankit model available here: http://hdl.handle.net/11356/1965. The latter was trained on both spoken (SST) and written (SSJ) data, and demonstrates a significantly higher performance to the model featured in this submission.

  • The Trankit model for linguistic process of standard written Slovenian 1.1

    This is a retrained Slovenian model for the Trankit v1.1.1 library for multilingual natural language processing (https://pypi.org/project/trankit/), trained on the reference SSJ UD treebank featuring fiction, non-fiction, periodical and Wikipedia texts in standard modern Slovenian. It is able to predict sentence segmentation, tokenization, lemmatization, language-specific morphological annotation (MULTEXT-East morphosyntactic tags), as well as universal part-of-speech tagging, morphological features, and dependency parses in accordance with the Universal Dependencies annotation scheme (https://universaldependencies.org/). The model was trained using a dataset published by Universal Dependencies in release 2.14 (https://github.com/UniversalDependencies/UD_Slovenian-SSJ/tree/r2.14). To utilize this model, please follow the instructions provided in our github repository (https://github.com/clarinsi/trankit-train) or refer to the Trankit documentation (https://trankit.readthedocs.io/en/latest/training.html#loading). This ZIP file contains models for both xlm-roberta-large (which delivers better performance but requires more hardware resources) and xlm-roberta-base. This version was trained on a newer, slightly improved version of the SSJ UD treebank (UD v2.14) than the previous version of the model and produces similar results.

  • SELEXINI corpus

    We present here a large automatically annotated corpus for French. This corpus is divided into two parts: the first from BigScience, and the second from HPLT. The annotated documents from HPLT were selected in order to optimise the lexical diversity of the final corpus SELEXINI.