Basic language processing: Difference between revisions
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Under basis language processing, we understand part-of-speech tagging, lemmatization, named entity recognition, chunking and similar tasks which label individual words. | Under basis language processing, we understand part-of-speech tagging, lemmatization, named entity recognition, chunking and similar tasks which label individual words. | ||
== Frog == | == Frog == <!--T:2--> | ||
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Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch. Frog's current version will tokenize, tag, lemmatize, and morphologically segment word tokens in Dutch text files, will assign a dependency graph to each sentence, will identify the base phrase chunks in the sentence, and will attempt to find and label all named entities. | Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch. Frog's current version will tokenize, tag, lemmatize, and morphologically segment word tokens in Dutch text files, will assign a dependency graph to each sentence, will identify the base phrase chunks in the sentence, and will attempt to find and label all named entities. | ||
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*[https://webservices.cls.ru.nl/frog Online version] | *[https://webservices.cls.ru.nl/frog Online version] | ||
*[https://languagemachines.github.io/frog/ Project website] | *[https://languagemachines.github.io/frog/ Project website] | ||
== DeepFrog == | == DeepFrog == <!--T:4--> | ||
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DeepFrog aims to be a (partial) successor of the Dutch-NLP suite Frog. Whereas the various NLP modules in Frog were built on k-NN classifiers, DeepFrog builds on deep learning techniques and can use a variety of neural transformers. | DeepFrog aims to be a (partial) successor of the Dutch-NLP suite Frog. Whereas the various NLP modules in Frog were built on k-NN classifiers, DeepFrog builds on deep learning techniques and can use a variety of neural transformers. | ||
The system is not yet officially released | <!--T:5--> | ||
*[https://huggingface.co/proycon/robbert-pos-cased-deepfrog-nld POS tagger] | |||
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The system is not yet officially released. | |||
*[https://github.com/proycon/deepfrog Github page] | *[https://github.com/proycon/deepfrog Github page] | ||
== LeTs == | == LeTs == <!--T:7--> | ||
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LeTs is preprocessor that can be used for Dutch, German, English and French. | LeTs is preprocessor that can be used for Dutch, German, English and French. | ||
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*[https://lt3.ugent.be/resources/lets/ Project page] | *[https://lt3.ugent.be/resources/lets/ Project page] | ||
*[https://lt3.ugent.be/lets-demo/ Demo] | *[https://lt3.ugent.be/lets-demo/ Demo] | ||
==Adelheid Tagger-Lemmatizer: A Distributed Lemmatizer for Historical Dutch== | == Spacy == <!--T:9--> | ||
Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner. | |||
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[https://spacy.io/models/nl Dutch models] | |||
== Stanza - A Python NLP Package for Many Human Languages == <!--T:11--> | |||
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Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. | |||
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* [https://stanfordnlp.github.io/stanza/#stanza--a-python-nlp-package-for-many-human-languages Stanza github pages] | |||
==Trankit== <!--T:20--> | |||
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Trankit is a light-weight Transformer-based Python Toolkit for multilingual Natural Language Processing (NLP). It provides a trainable pipeline for fundamental NLP tasks over 100 languages, and 90 downloadable pretrained pipelines for 56 languages. | |||
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*[https://github.com/nlp-uoregon/trankit Trankit Github pages] | |||
==Adelheid Tagger-Lemmatizer: A Distributed Lemmatizer for Historical Dutch== <!--T:13--> | |||
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With this web-application an end user can have historical Dutch texts tokenized, lemmatized and part-of-speech tagged, using the most appropriate resources (such as lexica) for the text in question. For each specific text, the user can select the best resources from those available in CLARIN, wherever they might reside, and where necessary supplemented by own lexica. | With this web-application an end user can have historical Dutch texts tokenized, lemmatized and part-of-speech tagged, using the most appropriate resources (such as lexica) for the text in question. For each specific text, the user can select the best resources from those available in CLARIN, wherever they might reside, and where necessary supplemented by own lexica. | ||
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*[http://portal.clarin.nl/node/1918 CLAPOP page] | *[http://portal.clarin.nl/node/1918 CLAPOP page] | ||
*No working version found | *No working version found | ||
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Latest revision as of 15:31, 6 November 2024
Under basis language processing, we understand part-of-speech tagging, lemmatization, named entity recognition, chunking and similar tasks which label individual words.
Frog
Frog is an integration of memory-based natural language processing (NLP) modules developed for Dutch. Frog's current version will tokenize, tag, lemmatize, and morphologically segment word tokens in Dutch text files, will assign a dependency graph to each sentence, will identify the base phrase chunks in the sentence, and will attempt to find and label all named entities.
DeepFrog
DeepFrog aims to be a (partial) successor of the Dutch-NLP suite Frog. Whereas the various NLP modules in Frog were built on k-NN classifiers, DeepFrog builds on deep learning techniques and can use a variety of neural transformers.
The system is not yet officially released.
LeTs
LeTs is preprocessor that can be used for Dutch, German, English and French.
Spacy
Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
Stanza - A Python NLP Package for Many Human Languages
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages.
Trankit
Trankit is a light-weight Transformer-based Python Toolkit for multilingual Natural Language Processing (NLP). It provides a trainable pipeline for fundamental NLP tasks over 100 languages, and 90 downloadable pretrained pipelines for 56 languages.
Adelheid Tagger-Lemmatizer: A Distributed Lemmatizer for Historical Dutch
With this web-application an end user can have historical Dutch texts tokenized, lemmatized and part-of-speech tagged, using the most appropriate resources (such as lexica) for the text in question. For each specific text, the user can select the best resources from those available in CLARIN, wherever they might reside, and where necessary supplemented by own lexica.
- CLAPOP page
- No working version found