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2. ABC - Language Identifier

Publisher:
Research Institute for Artificial Intelligence, Romanian Academy of Sciences
Type:
toolService
Description:
The application, developed in C#, automatically identifies the language of a text written in one of the 21 European Union languages. By using training texts in different languages (approx. 1.5Mb of text for each language), a training module counts the prefixes (the first 3 characters) and the suffixes (4 characters endings) for all the words in the texts, for each language. For every language two models are constructed, containing the weights (percentages) of prefixes and suffixes in the texts representing a language. In the prediction phase, for a new text, two models are built on the fly in a similar manner. These models are then compared with the stored models representing each language for which the application was trained. Using comparison functions, the best model is chose. More detailed descriptions are available in [[http://www.racai.ro/~tufis/papers|the following papers]]: -- Dan Tufiş, Radu Ion, Alexandru Ceauşu, and Dan Ştefănescu (2008). RACAI's Linguistic Web Services. In Proceedings of the 6th Language Resources and Evaluation Conference - LREC 2008, Marrakech, Morocco, May 2008. ELRA - European Language Resources Association. ISBN 2-9517408-4-0. -- Dan Tufiş and Alexandru Ceauşu (2007). Diacritics Restoration in Romanian Texts. In Elena Paskaleva and Milena Slavcheva (eds.), A Common Natural Language Processing Paradigm for Balkan Languages - RANLP 2007 Workshop Proceedings, pp. 49-56, Borovets, Bulgaria, September 2007. INCOMA Ltd., Shoumen, Bulgaria. ISBN 978-954-91743-8-0. -- Dan Tufiş and Adrian Chiţu (1999). Automatic Insertion of Diacritics in Romanian Texts. In Ferenc Kiefer, Gábor Kiss, and Júlia Pajzs (eds.), Proceedings of the 5th International Workshop on Computational Lexicography (COMPLEX 1999), pp. 185-194, Pecs, Hungary, May 1999. Linguistics Institute, Hungarian Academy of Sciences.
Rights:
Not specified

5. ANNIS

Publisher:
University of Potsdam, Dept. of Linguistics and Humboldt-University Berlin, Institut für deutsche Sprache und Linguistik
Type:
toolService
Description:
ANNIS2 is an open source, versatile web browser-based search and visualization architecture for complex multilevel linguistic corpora with diverse types of annotation. ANNIS, which stands for ANNotation of Information Structure, has been designed to provide access to the data of the SFB 632 - "Information Structure: The Linguistic Means for Structuring Utterances, Sentences and Texts". Since information structure interacts with linguistic phenomena on many levels, ANNIS2 addresses the SFB's need to concurrently annotate, query and visualize data from such varied areas as syntax, semantics, morphology, prosody, referentiality, lexis and more. For project working with spoken language, support for audio / video annotations is also required.
Rights:
Not specified

6. Annotate

Creator:
Roček, Martin
Publisher:
Charles University, Faculty of Arts
Type:
TEXT and toolService
Subject:
manuscripts, annotation, application, TEI, and JavaScript
Language:
No linguistic content
Description:
Annotate is a web and desktop application that should simplify the process of transforming photos of manuscripts to a browsable collection. It also allows users to annotate parts of the displayed images.
Rights:
GNU Library or "Lesser" General Public License 3.0 (LGPL-3.0), http://opensource.org/licenses/LGPL-3.0, and PUB

9. Araucaria

Publisher:
School of Computing, University of Dundee
Type:
toolService
Subject:
argument analyzer
Description:
Araucaria is a software tool for analysing arguments. It aids a user in reconstructing and diagramming an argument using a simple point-and-click interface. The software also supports argumentation schemes, and provides a user-customisable set of schemes with which to analyse arguments. Written in Java, released under the GNU General Public License.
Rights:
Not specified

15. BulTreeBank Morphological Analyzer

Creator:
Simov, Kiril and Osenova, Petya
Publisher:
Linguistic Modeling Department, IPP, Bulgarian Academy of Sciences
Type:
toolService
Description:
It is used morphological lexicon of Bulgarian (100 000 lemmas) compiled as a finite-state automaton in CLaRK System. It requires the text to be first tokenized and it is applied in each token. Includes also guessers for unknown words and Named Entities gazetteers. If the corresponding resources are available for a different language, then it can be tuned to it.
Rights:
Not specified

16. BulTreeBank Morphosyntactic Disambiguator

Creator:
Simov, Kiril, Osenova, Petya, and Simov, Alex
Publisher:
Linguistic Modeling Department, IPP, Bulgarian Academy of Sciences
Type:
toolService
Description:
This is a hybrid system: rules, neural network, rules. First rules for the sure cases are applied, then a neural network disambiguator is applied, then rules for repairing of the most frequent errors of the neural network. The rules are implemented as constraints in CLaRK System. The neural network is additional module implemented in Java. It is called CLaRK. It requires the morphologically annotated input.
Rights:
Not specified

21. calcular_p_cue_class

Publisher:
Institut Universitari de Lingüística Aplicada, Universitat Pompeu Fabra
Type:
toolService
Description:
Statistical analysis service: It calculates P(cue|class): probability of seeing a linguistic cue given a lexical class. This probability is computed given the occurrences of cues in a corpus (codified in the signatures file) and the information of belonging or not belonging of these words to different classes (codified in indicators file). The probability is computed for each studied cue in the signatures file and for each class in the indicators file.
Rights:
Not specified

22. Catalan Annotated Corpora CQP

Publisher:
Institut Universitari de Lingüística Aplicada, Universitat Pompeu Fabra
Type:
toolService
Description:
This RESTful service allows to define a sub-corpus from different annotated corpora. The service includes a POS tag harmonisation process where original tags are converted to EAGLES/Parole format. The eventual sub-corpus is indexed using the IMS CWB tool. The user receives an ID which can be used by the CQP service to exploit the sub-corpus.
Rights:
Not specified

27. Česílko

Creator:
Hajič, Jan, Kuboň, Vladislav, and Homola, Petr
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
toolService
Subject:
machine translation and Czech-Slovak translation
Language:
Czech
Description:
Česílko is a tool enabling the fast and efficient translation from one source language into many target languages, which are mutually related.
Rights:
Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0), http://creativecommons.org/licenses/by-nc-nd/3.0/, and PUB

28. Česílko 2.0 Shallow Transfer RBMT framework (opensource version)

Creator:
Vičič, Jernej, Kuboň, Vladislav, and Homola, Petr
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
tool and toolService
Subject:
Shallow Parse, Shallow Transfer Rule-Based Machine Translation, stochastic ranker, related languages, and toolbox
Description:
The system Česílko (language data and software tools) was first developed as an answer to a growing need of translation and localisation from one source language to many target languages. The starting system belonged to the Shallow Parse, Shallow Transfer Rule-Based Machine Translation – (RBMT) paradigm and it was designed primarily for translation of related languages. The latest implementation of the system uses a stochastic ranker; so technically it belongs to the hybrid machine translation paradigm, using stochastic methods combined with the traditional Shallow Transfer RBMT methods. The system has been stripped of the accompanying language resources due to copyright restrictions. The data that is available is just for demonstrative purposes.
Rights:
Not specified

30. Chared

Creator:
Pomikálek, Jan
Publisher:
Masaryk University, NLP Centre
Type:
toolService and tool
Subject:
character encoding, character encoding detection, charset, and unicode
Language:
English
Description:
Chared is a software tool which can detect character encoding of a text document provided the language of the document is known. The language of the text has to be specified as an input parameter so that the corresponding language model can be used. The package contains models for a wide range of languages (currently 57 --- covering all major languages). Furthermore, it provides a training script to learn models for additional languages using a set of user supplied sample html pages in the given language. The detection algorithm is based on determining similarity of byte trigrams vectors. In general, chared should be more accurate than other character encoding detection tools with no language constraints. This is an important advantage allowing precise character decoding needed for building large textual corpora. The tool has been used for building corpora in American Spanish, Arabic, Czech, French, Japanese, Russian, Tajik, and six Turkic languages consisting of 70 billions tokens altogether. Chared is an open source software, licensed under New BSD License and available for download (including the source code) at http://code.google.com/p/chared/. The research leading to this piece of software was published in POMIKÁLEK, Jan a Vít SUCHOMEL. chared: Character Encoding Detection with a Known Language. In Aleš Horák, Pavel Rychlý. RASLAN 2011. 5. vyd. Brno, Czech Republic: Tribun EU, 2011. od s. 125-129, 5 s. ISBN 978-80-263-0077-9. and PRESEMT, Lexical Computing Ltd
Rights:
BSD 3-Clause "New" or "Revised" license, http://opensource.org/licenses/BSD-3-Clause, and PUB

31. CLaRK System - an XML-based system for Corpora Development

Publisher:
Linguistic Modeling Department, IPP, Bulgarian Academy of Sciences
Type:
toolService
Subject:
corpus development
Description:
The CLaRK System incorporates several technologies: - XML technology - Unicode - Cascaded Regular Grammars; - Constraints over XML Documents On the basis of these technologies the following tools are implemented: XML Editor, Unicode Tokeniser, Sorting tool, Removing and Extracting tool, Concordancer, XSLT tool, Cascaded Regular Grammar tool, etc. 1 Unicode tokenization In order to provide possibility for imposing constraints over the textual node and to segment them in meaningful way, the CLaRK System supports a user-defined hierarchy of tokenisers. At the very basic level the user can define a tokeniser in terms of a set of token types. In this basic tokeniser each token type is defined by a set of UNICODE symbols. Above this basic level tokenisers, the user can define other tokenisers, for which the token types are defined as regular expressions over the tokens of some other tokeniser, the so called parent tokeniser. 2 Regular Grammars The regular grammars are the basic mechanism for linguistic processing of the content of an XML document within the system. The regular grammar processor applies a set of rules over the content of some elements in the document and incorporates the categories of the rules back in the document as XML mark-up. The content is processed before the application of the grammar rules in the following way: textual nodes are tokenized with respect to some appropriate tokeniser, the element nodes are textualized on the basis of XPath expressions that determine the important information about the element. The recognized word is substituted by a new XML mark-up, which can or can not contain the word. 3 Constraints The constraints that we implemented in the CLaRK System are generally based on the XPath language. We use XPath expressions to determine some data within one or several XML documents and thus we evaluate some predicates over the data. There are two modes of using a constraint. In the first mode the constraint is used for validity check, similar to the validity check, which is based on DTD or XML schema. In the second mode, the constraint is used to support the change of the document in order it to satisfy the constraint. There are three types of constraints, implemented in the system: regular expression constraints, number restriction constraints, value restriction constraints. 4 Macro Language In the CLaRK System the tools support a mechanism for describing their settings. On the basis of these descriptions (called queries) a tool can be applied only by pointing to a certain description record. Each query contains the states of all settings and options which the corresponding tool has. Once having this kind of queries there is a special tool for combining and applying them in groups (macros). During application the queries are executed successively and the result from an application is an input for the next one. For a better control on the process of applying several queries in one we introduce several conditional operators. These operators can determine the next query for application depending on certain conditions. When a condition for such an operator is satisfied, the execution continues from a location defined in the operator. The mechanism for addressing queries is based on user defined labels. When a condition is not satisfied the operator is ignored and the process continues from the position following the operator. In this way constructions like IF-THEN-ELSE and WHILE-DO easily can be expressed. The system supports five types of control operators: IF (XPath): the condition is an XPath expression which is evaluated on the current working document. If the result is a non-empty node-set, non-empty string, positive number or true boolean value the condition is satisfied; IF NOT (XPath): the same kind of condition as the previous one but the approving result is negated; IF CHANGED: the condition is satisfied if the preceding operation has changed the current working document or has produced a non-empty result document (depending on the operation); IF NOT CHANGED: the condition is satisfied if either the previous operation did not change the working document or did not produce a non-empty result. GOTO: unconditional changing the execution position. Each macro defined in the system can have its own query and can be incorporated in another macro. In this way some limited form of subroutine can be implemented. The new version of CLaRK will support server applications, calls to/from external programs.
Rights:
Not specified

32. CLaRK System - XML-based system for Corpora Development

Creator:
Simov, Kiril, Simov, Alex, and Kouylekov, Milen
Publisher:
Linguistic Modeling Department, IPP, Bulgarian Academy of Sciences
Type:
toolService
Description:
The CLaRK System incorporates several technologies: - XML technology - Unicode - Cascaded Regular Grammars; - Constraints over XML Documents On the basis of these technologies the following tools are implemented: XML Editor, Unicode Tokeniser, Sorting tool, Removing and Extracting tool, Concordancer, XSLT tool, Cascaded Regular Grammar tool, etc. 1 Unicode tokenization In order to provide possibility for imposing constraints over the textual node and to segment them in meaningful way, the CLaRK System supports a user-defined hierarchy of tokenisers. At the very basic level the user can define a tokeniser in terms of a set of token types. In this basic tokeniser each token type is defined by a set of UNICODE symbols. Above this basic level tokenisers, the user can define other tokenisers, for which the token types are defined as regular expressions over the tokens of some other tokeniser, the so called parent tokeniser. 2 Regular Grammars The regular grammars are the basic mechanism for linguistic processing of the content of an XML document within the system. The regular grammar processor applies a set of rules over the content of some elements in the document and incorporates the categories of the rules back in the document as XML mark-up. The content is processed before the application of the grammar rules in the following way: textual nodes are tokenized with respect to some appropriate tokeniser, the element nodes are textualized on the basis of XPath expressions that determine the important information about the element. The recognized word is substituted by a new XML mark-up, which can or can not contain the word. 3 Constraints The constraints that we implemented in the CLaRK System are generally based on the XPath language. We use XPath expressions to determine some data within one or several XML documents and thus we evaluate some predicates over the data. There are two modes of using a constraint. In the first mode the constraint is used for validity check, similar to the validity check, which is based on DTD or XML schema. In the second mode, the constraint is used to support the change of the document in order it to satisfy the constraint. There are three types of constraints, implemented in the system: regular expression constraints, number restriction constraints, value restriction constraints. 4 Macro Language In the CLaRK System the tools support a mechanism for describing their settings. On the basis of these descriptions (called queries) a tool can be applied only by pointing to a certain description record. Each query contains the states of all settings and options which the corresponding tool has. Once having this kind of queries there is a special tool for combining and applying them in groups (macros). During application the queries are executed successively and the result from an application is an input for the next one. For a better control on the process of applying several queries in one we introduce several conditional operators. These operators can determine the next query for application depending on certain conditions. When a condition for such an operator is satisfied, the execution continues from a location defined in the operator. The mechanism for addressing queries is based on user defined labels. When a condition is not satisfied the operator is ignored and the process continues from the position following the operator. In this way constructions like IF-THEN-ELSE and WHILE-DO easily can be expressed. The system supports five types of control operators: IF (XPath): the condition is an XPath expression which is evaluated on the current working document. If the result is a non-empty node-set, non-empty string, positive number or true boolean value the condition is satisfied; IF NOT (XPath): the same kind of condition as the previous one but the approving result is negated; IF CHANGED: the condition is satisfied if the preceding operation has changed the current working document or has produced a non-empty result document (depending on the operation); IF NOT CHANGED: the condition is satisfied if either the previous operation did not change the working document or did not produce a non-empty result. GOTO: unconditional changing the execution position. Each macro defined in the system can have its own query and can be incorporated in another macro. In this way some limited form of subroutine can be implemented. The new version of CLaRK will support server applications, calls to/from external programs.
Rights:
Not specified

34. CorPipe 23 multilingual CorefUD 1.1 model (corpipe23-corefud1.1-231206)

Creator:
Straka, Milan
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
tool and toolService
Subject:
coreference resolution, CorPipe, and CorefUD
Language:
Catalan, Czech, German, English, Spanish, French, Hungarian, Lithuanian, Norwegian Bokmål, Norwegian Nynorsk, Polish, Russian, and Turkish
Description:
The `corpipe23-corefud1.1-231206` is a `mT5-large`-based multilingual model for coreference resolution usable in CorPipe 23 (https://github.com/ufal/crac2023-corpipe). It is released under the CC BY-NC-SA 4.0 license. The model is language agnostic (no _corpus id_ on input), so it can be used to predict coreference in any `mT5` language (for zero-shot evaluation, see the paper). However, note that the empty nodes must be present already on input, they are not predicted (the same settings as in the CRAC23 shared task).
Rights:
Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

36. Corpus Work Bench CWB (CQP)

Publisher:
Institut Universitari de Lingüística Aplicada, Universitat Pompeu Fabra
Type:
toolService
Description:
This SOAP service implements the IMS Open Corpus Workbench (CWB), a collection of open-source tools for managing and querying large text corpora (ranging from 10 million to 2 billion words) with linguistic annotations. Its central component is the flexible and efficient query processor CQP. The service makes it possible to index a new corpus and query it.
Rights:
Not specified

37. CorpusExplorer

Creator:
Rüdiger, Jan Oliver
Publisher:
Jan Oliver Rüdiger
Type:
tool and toolService
Subject:
Corpus Linguisitics, NLP, conll, tei, XML, nlp, Natural Language Processing, linguistics, Linguistics, Computational Linguistics, corpus processing, tagger, POS tagger, lemmatization, text cleaning, CommonCrawl, epub, JSON, Twitter, Pandoc, Wikipedia, digital data, DTA, DSpin, MySQL, ElasticSearch, TextGrid, text corpora, TigerXML, and WeblichtXML
Language:
German, English, French, Italian, Dutch, Spanish, Polish, Arabic, Chinese, and Portuguese
Description:
Software for corpus linguists and text/data mining enthusiasts. The CorpusExplorer combines over 45 interactive visualizations under a user-friendly interface. Routine tasks such as text acquisition, cleaning or tagging are completely automated. The simple interface supports the use in university teaching and leads users/students to fast and substantial results. The CorpusExplorer is open for many standards (XML, CSV, JSON, R, etc.) and also offers its own software development kit (SDK). Source code available at https://github.com/notesjor/corpusexplorer2.0
Rights:
Not specified

41. CUBBITT Translation Models (en-cs) (v1.0)

Creator:
Popel, Martin, Tomková, Markéta, Tomek, Jakub, Kaiser, Łukasz, Uszkoreit, Jakob, Bojar, Ondřej, and Žabokrtský, Zdeněk
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
tool and toolService
Subject:
machine translation, neural machine translation, transformer, and cubbitt
Language:
English and Czech
Description:
CUBBITT En-Cs translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). Models are compatible with Tensor2tensor version 1.6.6. For details about the model training (data, model hyper-parameters), please contact the archive maintainer. Evaluation on newstest2014 (BLEU): en->cs: 27.6 cs->en: 34.4 (Evaluated using multeval: https://github.com/jhclark/multeval)
Rights:
Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

42. CUBBITT Translation Models (en-fr) (v1.0)

Creator:
Popel, Martin, Tomková, Markéta, Tomek, Jakub, Kaiser, Łukasz, Uszkoreit, Jakob, Bojar, Ondřej, and Žabokrtský, Zdeněk
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
tool and toolService
Subject:
machine translation, neural machine translation, transformer, and cubbitt
Language:
English and French
Description:
CUBBITT En-Fr translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). Models are compatible with Tensor2tensor version 1.6.6. For details about the model training (data, model hyper-parameters), please contact the archive maintainer. Evaluation on newstest2014 (BLEU): en->fr: 38.2 fr->en: 36.7 (Evaluated using multeval: https://github.com/jhclark/multeval)
Rights:
Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

43. CUBBITT Translation Models (en-pl) (v1.0)

Creator:
Popel, Martin, Tomková, Markéta, Tomek, Jakub, Kaiser, Łukasz, Uszkoreit, Jakob, Bojar, Ondřej, and Žabokrtský, Zdeněk
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
tool and toolService
Subject:
machine translation, neural machine translation, transformer, and cubbitt
Language:
English and Polish
Description:
CUBBITT En-Pl translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). Models are compatible with Tensor2tensor version 1.6.6. For details about the model training (data, model hyper-parameters), please contact the archive maintainer. Evaluation on newstest2020 (BLEU): en->pl: 12.3 pl->en: 20.0 (Evaluated using multeval: https://github.com/jhclark/multeval)
Rights:
Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

45. Czech image captioning, machine translation, and sentiment analysis (Neural Monkey models)

Creator:
Libovický, Jindřich, Rosa, Rudolf, Helcl, Jindřich, and Popel, Martin
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
suiteOfTools and toolService
Subject:
sentiment analysis, machine translation, image captioning, neural networks, transformer, and Neural Monkey
Language:
Czech and English
Description:
This submission contains trained end-to-end models for the Neural Monkey toolkit for Czech and English, solving three NLP tasks: machine translation, image captioning, and sentiment analysis. The models are trained on standard datasets and achieve state-of-the-art or near state-of-the-art performance in the tasks. The models are described in the accompanying paper. The same models can also be invoked via the online demo: https://ufal.mff.cuni.cz/grants/lsd There are several separate ZIP archives here, each containing one model solving one of the tasks for one language. To use a model, you first need to install Neural Monkey: https://github.com/ufal/neuralmonkey To ensure correct functioning of the model, please use the exact version of Neural Monkey specified by the commit hash stored in the 'git_commit' file in the model directory. Each model directory contains a 'run.ini' Neural Monkey configuration file, to be used to run the model. See the Neural Monkey documentation to learn how to do that (you may need to update some paths to correspond to your filesystem organization). The 'experiment.ini' file, which was used to train the model, is also included. Then there are files containing the model itself, files containing the input and output vocabularies, etc. For the sentiment analyzers, you should tokenize your input data using the Moses tokenizer: https://pypi.org/project/mosestokenizer/ For the machine translation, you do not need to tokenize the data, as this is done by the model. For image captioning, you need to: - download a trained ResNet: http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz - clone the git repository with TensorFlow models: https://github.com/tensorflow/models - preprocess the input images with the Neural Monkey 'scripts/imagenet_features.py' script (https://github.com/ufal/neuralmonkey/blob/master/scripts/imagenet_features.py) -- you need to specify the path to ResNet and to the TensorFlow models to this script Feel free to contact the authors of this submission in case you run into problems!
Rights:
Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

46. Czech image captioning, machine translation, sentiment analysis and summarization (Neural Monkey models)

Creator:
Libovický, Jindřich, Rosa, Rudolf, Helcl, Jindřich, and Popel, Martin
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
suiteOfTools and toolService
Subject:
sentiment analysis, machine translation, image captioning, neural networks, transformer, Neural Monkey, and summarization
Language:
Czech and English
Description:
This submission contains trained end-to-end models for the Neural Monkey toolkit for Czech and English, solving four NLP tasks: machine translation, image captioning, sentiment analysis, and summarization. The models are trained on standard datasets and achieve state-of-the-art or near state-of-the-art performance in the tasks. The models are described in the accompanying paper. The same models can also be invoked via the online demo: https://ufal.mff.cuni.cz/grants/lsd In addition to the models presented in the referenced paper (developed and published in 2018), we include models for automatic news summarization for Czech and English developed in 2019. The Czech models were trained using the SumeCzech dataset (https://www.aclweb.org/anthology/L18-1551.pdf), the English models were trained using the CNN-Daily Mail corpus (https://arxiv.org/pdf/1704.04368.pdf) using the standard recurrent sequence-to-sequence architecture. There are several separate ZIP archives here, each containing one model solving one of the tasks for one language. To use a model, you first need to install Neural Monkey: https://github.com/ufal/neuralmonkey To ensure correct functioning of the model, please use the exact version of Neural Monkey specified by the commit hash stored in the 'git_commit' file in the model directory. Each model directory contains a 'run.ini' Neural Monkey configuration file, to be used to run the model. See the Neural Monkey documentation to learn how to do that (you may need to update some paths to correspond to your filesystem organization). The 'experiment.ini' file, which was used to train the model, is also included. Then there are files containing the model itself, files containing the input and output vocabularies, etc. For the sentiment analyzers, you should tokenize your input data using the Moses tokenizer: https://pypi.org/project/mosestokenizer/ For the machine translation, you do not need to tokenize the data, as this is done by the model. For image captioning, you need to: - download a trained ResNet: http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz - clone the git repository with TensorFlow models: https://github.com/tensorflow/models - preprocess the input images with the Neural Monkey 'scripts/imagenet_features.py' script (https://github.com/ufal/neuralmonkey/blob/master/scripts/imagenet_features.py) -- you need to specify the path to ResNet and to the TensorFlow models to this script The summarization models require input that is tokenized with Moses Tokenizer (https://github.com/alvations/sacremoses) and lower-cased. Feel free to contact the authors of this submission in case you run into problems!
Rights:
Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

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

Creator:
Straka, Milan
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
tool and toolService
Subject:
tokenizer, POS tagger, lemmatization, parser, dependency parser, MorfFlex CZ 2.0, and PDT-C 1.0
Language:
Czech
Description:
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 .
Rights:
Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB

50. Depfix: Automatic Post-editing of SMT

Creator:
Rosa, Rudolf
Publisher:
Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Type:
tool and toolService
Subject:
machine translation, post-editing, Treex, morphology, and parsing
Language:
English and Czech
Description:
Depfix, a tool for Automatic Post-editing of SMT. See the project website for more information.
Rights:
GNU General Public License, version 2, http://www.gnu.org/licenses/gpl-2.0.html, and PUB

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