Search results

• UDPipe

  1 resources

  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.

• Frog

  1 resources

  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.

  Use "Frog"

• PaQu

  3 resources

  Met PaQu (Parse & Query) kun je zoeken in syntactisch geannoteerde Nederlandstalige corpora. PaQu ondersteunt twee manieren van zoeken. Met de eerste, eenvoudige, manier kun je naar woordparen zoeken, met daarbij eventueel hun syntactische relatie. De tweede, ingewikkeldere, manier gebruikt de zoektaal XPath. In PaQu is een aantal syntactisch geannoteerde corpora standaard beschikbaar. Maar het is ook mogelijk om je eigen teksten aan te bieden. Deze teksten worden dan door de automatische ontleder geanalyseerd, en opgeslagen. Vervolgens kun je dan op dezelfde manier in je eigen teksten zoeken.

• Alpino-Webservice

  2 resources

  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.

  Use "Alpino-Webservice"

• AlpinoGraph

  3 resources

  AlpinoGraph is een tool om syntactisch geannoteerde corpora te doorzoeken. De tool maakt gebruik van AgensGraph. AgensGraph combineert databasetechnologie (PostgreSQL) en Cypher, de standaard zoektaal voor grafen. De zoek-queries die je in AlpinoGraph kunt gebruiken zijn daarom een mix van SQL en Cypher. Daar voegt AlpinoGraph nog enkele extra uitbreidingen aan toe, zoals een eenvoudig maar handig systeem van macro's, en visualisatie van de resultaten.

• Service for querying dependency treebanks Drevesnik 1.0

  2 resources

  Drevesnik (https://orodja.cjvt.si/drevesnik/) is an online service for querying syntactically parsed corpora in Slovenian using the Universal Dependencies annotation scheme with easy-to-use query language on the one hand and user-friendly graph visualizations on the other. It is based on the open-source dep_search tool (https://github.com/TurkuNLP/dep_search), which was localized and modified so as to also support querying by JOS morphosyntactic tags, random distribution of results, and filtering by sentence length. The source code and the documentation for the search backend and the web user interface are publicly available on the CLARIN.SI GitHub repository https://github.com/clarinsi/drevesnik. This submission corresponds to release 1.0: https://github.com/clarinsi/drevesnik/releases/tag/1.0.

  Use "Service for querying dependency treebanks Drevesnik 1.0"

• Trankit model for SST 2.15 1.1

  2 resources

  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.

  Use "Trankit model for SST 2.15 1.1"

• Parsito

  2 resources

  Parsito is a fast open-source dependency parser written in C++. Parsito is based on greedy transition-based parsing, it has very high accuracy and achieves a throughput of 30K words per second. Parsito can be trained on any input data without feature engineering, because it utilizes artificial neural network classifier. Trained models for all treebanks from Universal Dependencies project are available (37 treebanks as of Dec 2015). Parsito is a free software under Mozilla Public License 2.0 (http://www.mozilla.org/MPL/2.0/) and the linguistic models are free for non-commercial use and distributed under CC BY-NC-SA (http://creativecommons.org/licenses/by-nc-sa/4.0/) license, although for some models the original data used to create the model may impose additional licensing conditions. Parsito website http://ufal.mff.cuni.cz/parsito contains download links of both the released packages and trained models, hosts documentation and offers online demo. Parsito development repository http://github.com/ufal/parsito is hosted on GitHub.

  Use "Parsito"

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

  2 resources

  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.

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

• Trankit model for SST 2.15

  2 resources

  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.

  Use "Trankit model for SST 2.15"