Basic language processing/en: Difference between revisions

Latest revision as of 07:18, 9 May 2025

Under basis language processing, we understand part-of-speech tagging, lemmatization, named entity recognition, chunking and similar tasks which label individual words.

Contemporary Dutch

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.

POS tagger

The system is not yet officially released.

Github page

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.

Dutch models

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 github pages

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.

Trankit Github pages

Historical Dutch

GaLaHaD: Generating Linguistic Annotations for Historical Dutch

GaLAHaD serves two purposes. One is to make annotation and tool evaluation easily accessible to researchers, the other to make it easy for developers to contribute their tools and models to the platform, and thus compare them to other tools with gold standard material.

Webservice

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

@@ Line 2: / Line 2: @@
 Under basis language processing, we understand part-of-speech tagging, lemmatization, named entity recognition, chunking and similar tasks which label individual words.
-== Frog ==
+==Contemporary Dutch==
+=== 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.
@@ Line 9: / Line 11: @@
 *[https://languagemachines.github.io/frog/ Project website]
-== 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.
@@ Line 18: / Line 20: @@
 *[https://github.com/proycon/deepfrog Github page]
-== LeTs ==
+=== LeTs ===
 LeTs is preprocessor that can be used for Dutch, German, English and French.
@@ Line 25: / Line 27: @@
 *[https://lt3.ugent.be/lets-demo/ Demo]
-== Spacy ==
+=== Spacy ===
+Components: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
 [https://spacy.io/models/nl Dutch models]
-== 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.
@@ Line 35: / Line 39: @@
 * [https://stanfordnlp.github.io/stanza/#stanza--a-python-nlp-package-for-many-human-languages Stanza github pages]
-==Adelheid Tagger-Lemmatizer:  A Distributed Lemmatizer for Historical Dutch==
+===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.
+*[https://github.com/nlp-uoregon/trankit Trankit Github pages]
+==Historical Dutch==
+===GaLaHaD:  Generating Linguistic Annotations for Historical Dutch===
+GaLAHaD serves two purposes. One is to make annotation and tool evaluation easily accessible to researchers, the other to make it easy for developers to contribute their tools and models to the platform, and thus compare them to other tools with gold standard material.
+[https://portal.clarin.ivdnt.org/galahad Webservice]
+===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.