Basic language processing: Difference between revisions
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== Frog == | == 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. | 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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== DeepFrog == | == 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. | 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. | ||
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== LeTs == | == LeTs == | ||
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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== Stanza - A Python NLP Package for Many Human Languages == | == 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. | Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. | ||
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==Adelheid Tagger-Lemmatizer: A Distributed Lemmatizer for Historical Dutch== | ==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. | 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. | ||
Revision as of 10:08, 30 May 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
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.
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