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• Miðeind's Neural Constituency Parser - v. 1.0

  2 resources

  The Miðeind neural constituency parser is an experimental variant of the Berkeley neural parser architecture. It is self-contained and conveniently plug-and-play via a docker image. Currently POS tags are not part of its constituency trees. The input to the parser is a full path to a text file (${INPUT_FILE}) where each line contains a sentence that will be parsed. No prior tokenization is required. The output file will be located in ${OUTPUT_DIR}/output.txt and the output format is line-separated bracketed trees . To run the parser use the following: docker run --volume ${INPUT_FILE}:/data/input.txt --volume ${OUTPUT_DIR}:/data/ mideind/neural-parser:${TAG} The output follows the bracketed tree format described at https://www.ling.upenn.edu/~janabeck/tutorial.html --- Tauganetsþáttari Miðeindar er tilraunaafbrigði af Berkeley tauganetsþáttaranum. Þáttarinn skilar stofnliðatrjám án POS-marka (eins og er). Inntakið í þáttarann er full algjör slóð texta að skrá (${INPUT_FILE}) þar sem hver lína geymir eina málsgrein. Eftir keyrslu má finna úttakið í skránni ${OUTPUT_DIR}/output.txt þar sem úttakssniðið er tré á svigaformi með auðri línu á milli . Til að keyra þáttarann skal nota: docker run --volume ${INPUT_FILE}:/data/input.txt --volume ${OUTPUT_DIR}:/data/ mideind/neural-parser:${TAG} (edited)

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• Universal Dependencies 2.10 models for UDPipe 2 (2022-07-11)

  2 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for 123 treebanks of 69 languages of Universal Depenencies 2.10 Treebanks, created solely using UD 2.10 data (https://hdl.handle.net/11234/1-4758). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_210_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

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• Czech PDT-C 1.0 Model for UDPipe 2 (2023-11-16)

  2 resources

  Tokenizer, POS Tagger, Lemmatizer, and Parser model based on the PDT-C 1.0 treebank (https://hdl.handle.net/11234/1-3185). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#czech_pdtc1.0_model . To use these models, you need UDPipe version 2.1, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

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• Universal Dependencies 1.2 Models for Parsito

  2 resources

  Parsing models for all Universal Depenencies 1.2 Treebanks, created solely using UD 1.2 data (http://hdl.handle.net/11234/1-1548). To use these models, you need Parsito binary, which you can download from http://hdl.handle.net/11234/1-1584.

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• The Model latinpipe-evalatin24-240520 for LatinPipe 2024

  2 resources

  The latinpipe-evalatin24-240520 is a PhilBerta-based model for LatinPipe 2024 <https://github.com/ufal/evalatin2024-latinpipe>, performing tagging, lemmatization, and dependency parsing of Latin, based on the winning entry to the EvaLatin 2024 <https://circse.github.io/LT4HALA/2024/EvaLatin> shared task. It is released under the CC BY-NC-SA 4.0 license.

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• Universal Dependencies 2.6 models for UDPipe 2 (2020-08-31)

  2 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for 99 treebanks of 63 languages of Universal Depenencies 2.6 Treebanks, created solely using UD 2.6 data (https://hdl.handle.net/11234/1-3226). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_26_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

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• The CLASSLA-Stanza model for UD dependency parsing of standard Bulgarian 2.1

  3 resources

  The model for UD dependency parsing of standard Bulgarian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the UD-parsed portion of the BulTreeBank training corpus (https://clarino.uib.no/korpuskel/corpora) and using the CLARIN.SI-embed.bg word embeddings (http://hdl.handle.net/11356/1796). The estimated LAS of the parser is ~91.18. The difference to the previous version of the parser is that this version was trained using the new version of the Bulgarian word embeddings.

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• The CLASSLA-StanfordNLP model for UD dependency parsing of standard Croatian

  3 resources

  The model for UD dependency parsing of standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the UD-parsed portion of the hr500k training corpus (http://hdl.handle.net/11356/1183) and using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205). The estimated LAS of the parser is ~85.9.

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• The CLASSLA-Stanza model for JOS dependency parsing of standard Slovenian 2.0

  3 resources

  This model for JOS dependency parsing of standard Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SUK training corpus (http://hdl.handle.net/11356/1747) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204) expanded with the MaCoCu-sl Slovene web corpus (http://hdl.handle.net/11356/1517). The estimated LAS of the parser is ~93.89. The difference to the previous version of the model is that the model was trained using the SUK training corpus and uses the updated embeddings.

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• Universal Dependencies 1.2 Models for UDPipe

  2 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for all Universal Depenencies 1.2 Treebanks, created solely using UD 1.2 data (http://hdl.handle.net/11234/1-1548). To use these models, you need UDPipe binary, which you can download from http://ufal.mff.cuni.cz/udpipe.

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