This toolkit comprises the tools and supporting scripts for unsupervised induction of dependency trees from raw texts or texts with already assigned part-of-speech tags. There are also scripts for simple machine translation based on unsupervised parsing and scripts for minimally supervised parsing into Universal-Dependencies style.
Source code of the first full and running version for the Malach Center User Interface, does not contain data or metadata fo the digital objects and resources.
En-De translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitstream/handle/11509/142/mcsq_v3.zip).
Their main use should be in-domain translation of social surveys.
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 MCSQ test set (BLEU):
en->de: 67.5 (train: genuine in-domain MCSQ data only)
de->en: 75.0 (train: additional in-domain backtranslated MCSQ data)
(Evaluated using multeval: https://github.com/jhclark/multeval)
En-Ru translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitstream/handle/11509/142/mcsq_v3.zip).
Their main use should be in-domain translation of social surveys.
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 MCSQ test set (BLEU):
en->ru: 64.3 (train: genuine in-domain MCSQ data)
ru->en: 74.7 (train: additional backtranslated in-domain MCSQ data)
(Evaluated using multeval: https://github.com/jhclark/multeval)
This tool is the first morphological analyzer ever for this language.
The analyzer is a FST that produces all possible segmentations and tagging sequences in a word-by-word fashion.
MSTperl is a Perl reimplementation of the MST parser of Ryan McDonald (http://www.seas.upenn.edu/~strctlrn/MSTParser/MSTParser.html).
MST parser (Maximum Spanning Tree parser) is a state-of-the-art natural language dependency parser -- a tool that takes a sentence and returns its dependency tree.
In MSTperl, only some functionality was implemented; the limitations include the following:
the parser is a non-projective one, curently with no possibility of enforcing the requirement of projectivity of the parse trees;
only first-order features are supported, i.e. no second-order or third-order features are possible;
the implementation of MIRA is that of a single-best MIRA, with a closed-form update instead of using quadratic programming.
On the other hand, the parser supports several advanced features:
parallel features, i.e. enriching the parser input with word-aligned sentence in other language;
adding large-scale information, i.e. the feature set enriched with features corresponding to pointwise mutual information of word pairs in a large corpus (CzEng).
The MSTperl parser is tuned for parsing Czech. Trained models are available for Czech, English and German. We can train the parser for other languages on demand, or you can train it yourself -- the guidelines are part of the documentation.
The parser, together with detailed documentation, is avalable on CPAN (http://search.cpan.org/~rur/Treex-Parser-MSTperl/). and The research has been supported by the EU Seventh Framework Programme under grant agreement 247762 (Faust), and by the grants GAUK116310 and GA201/09/H057.
MSTperl is a Perl reimplementation of the MST parser of Ryan McDonald (http://www.seas.upenn.edu/~strctlrn/MSTParser/MSTParser.html).
MST parser (Maximum Spanning Tree parser) is a state-of-the-art natural language dependency parser -- a tool that takes a sentence and returns its dependency tree.
In MSTperl, only some functionality was implemented; the limitations include the following:
the parser is a non-projective one, curently with no possibility of enforcing the requirement of projectivity of the parse trees;
only first-order features are supported, i.e. no second-order or third-order features are possible;
the implementation of MIRA is that of a single-best MIRA, with a closed-form update instead of using quadratic programming.
On the other hand, the parser supports several advanced features:
parallel features, i.e. enriching the parser input with word-aligned sentence in other language;
adding large-scale information, i.e. the feature set enriched with features corresponding to pointwise mutual information of word pairs in a large corpus (CzEng);
weighted/unweighted parser model interpolation;
combination of several instances of the MSTperl parser (through MST algorithm);
combination of several existing parses from any parsers (through MST algorithm).
The MSTperl parser is tuned for parsing Czech. Trained models are available for Czech, English and German. We can train the parser for other languages on demand, or you can train it yourself -- the guidelines are part of the documentation.
The parser, together with detailed documentation, is avalable on CPAN (http://search.cpan.org/~rur/Treex-Parser-MSTperl/). and The research has been supported by the EU Seventh Framework Programme under grant agreement 247762 (Faust), and by the grants GAUK116310 and GA201/09/H057.
MUSCIMarker is an open-source tool for annotating visual objects and their relationships in binary images. It is implemented in Python, known to run on Windows, Linux and OS X, and supports working offline. MUSCIMarker is being used for creating a dataset of musical notation symbols, but can support any object set.
The user documentation online is currently (12.2016) incomplete, as it is continually changing to reflect annotators' comments and incorporate new features. This version of the software is *not* the final one, and it is under continuous development (we're currently working on adding grayscale image support with auto-binarization, and Android support for touch-based annotation). However, the current version (1.1) has already been used to annotate more than 100 pages of sheet music, over all the major desktop OSes, and I believe it is already in a state where it can be useful beyond my immediate music notation data gathering use case.
NameTag 2 is a named entity recognition tool. It recognizes named entities (e.g., names, locations, etc.) and can recognize both flat and embedded (nested) entities. NameTag 2 can be used either as a commandline tool or by requesting the NameTag webservice.
NameTag webservice can be found at:
https://lindat.mff.cuni.cz/services/nametag/
NameTag commandline tool can be downloaded from NameTag GitHub repository, branch nametag2:
git clone https://github.com/ufal/nametag -b nametag2
Latest models and documentation can be found at:
https://ufal.mff.cuni.cz/nametag/2
This software subject to the terms of the Mozilla Public License, v. 2.0 (http://mozilla.org/MPL/2.0/). The associated models are distributed under CC BY-NC-SA license.
Please cite as:
Jana Straková, Milan Straka, Jan Hajič (2019): Neural Architectures for Nested NER through Linearization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5326-5331, Association for Computational Linguistics, Stroudsburg, PA, USA, ISBN 978-1-950737-48-2 (https://aclweb.org/anthology/papers/P/P19/P19-1527/)
onion (ONe Instance ONly) is a tool for removing duplicate parts from large collections of texts. The tool has been implemented in Python, licensed under New BSD License and made an open source software (available for download including the source code at http://code.google.com/p/onion/). It is being successfuly used for cleaning large textual corpora at Natural language processing centre at Faculty of informatics, Masaryk university Brno and it's industry partners. The research leading to this piece of software was published in author's Ph.D. thesis "Removing Boilerplate and Duplicate Content from Web Corpora". The deduplication algorithm is based on comparing n-grams of words of text. The author's algorithm has been shown to be more suitable for textual corpora deduplication than competing algorithms (Broder, Charikar): in addition to detection of identical or very similar (95 %) duplicates, it is able to detect even partially similar duplicates (50 %) still achieving great performace (further described in author's Ph.D. thesis). The unique deduplication capabilities and scalability of the algorithm were been demonstrated while building corpora of American Spanish, Arabic, Czech, French, Japanese, Russian, Tajik, and six Turkic languages consisting --- several TB of text documents were deduplicated resulting in corpora of 70 billions tokens altogether. and PRESEMT, Lexical Computing Ltd