Slovak morphological dictionary modeled after the Czech one. It consists of (word form, lemma, POS tag) triples, reusing the Czech morphological system for POS tags and lemma descriptions.
The MORFO system for morphological analysis of Czech consists of four units: the analyzer, the generator, the dictionary editor, and the library with the shared source code for handling dictionary objects.
MorphoDiTa: Morphological Dictionary and Tagger is an open-source tool for morphological analysis of natural language texts. It performs morphological analysis, morphological generation, tagging and tokenization and is distributed as a standalone tool or a library, along with trained linguistic models. In the Czech language, MorphoDiTa achieves state-of-the-art results with a throughput around 10-200K words per second. MorphoDiTa is a free software under LGPL license and the linguistic models are free for non-commercial use and distributed under CC BY-NC-SA license, although for some models the original data used to create the model may impose additional licensing conditions.
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.
This is a set of MSTperl parser configuration files and scripts for delexicalized parser transfer. They were used in the work reported in arXiv:1506.04897 (http://arxiv.org/abs/1506.04897), as well as several related papers. The MSTperl parser is available at http://hdl.handle.net/11234/1-1480
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.
MTMonkey is a web service which handles and distributes JSON-encoded HTTP requests for machine translation (MT) among multiple machines running an MT system, including text pre- and post processing.
It consists of an application server and remote workers which handle text processing and communicate translation requests to MT systems. The communication between the application server and the workers is based on the XML-RPC protocol. and The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 257528 (KHRESMOI). This work has been using language resources developed and/or stored and/or distributed by the LINDAT-Clarin project of the Ministry of Education of the Czech Republic (project LM2010013). This work has been supported by the AMALACH grant (DF12P01OVV02) of the Ministry of Culture of the Czech Republic.
This dataset adds annotation of multiword expressions and multiword named entities to the original PDT 2.0 data. The annotation is stand-off, stored in the same PML format as the original PDT 2.0 data. It is to be used together with the PDT 2.0. and grant 1ET201120505 of the Academy of Sciences of the Czech Republic and grant MSM0021620838 of the Ministry of Youth, Education and Sport of The Czech Republic