AKCES-GEC is a grammar error correction corpus for Czech generated from a subset of AKCES. It contains train, dev and test files annotated in M2 format.
Note that in comparison to CZESL-GEC dataset, this dataset contains separated edits together with their type annotations in M2 format and also has two times more sentences.
If you use this dataset, please use following citation:
@article{naplava2019wnut,
title={Grammatical Error Correction in Low-Resource Scenarios},
author={N{\'a}plava, Jakub and Straka, Milan},
journal={arXiv preprint arXiv:1910.00353},
year={2019}
}
Automatically generated spelling correction corpus for Czech (Czesl-SEC-AG) is a corpus containg text with automatically generated spelling errors. To create spelling errors, a character error model containing probabilities of character substitution, insertion, deletion and probabilities of swaping two adjacent characters is used. Besides these probabilities, also the probabilities of changing character casing are considered. The original clean text on which the spelling errors were generated is PDT3.0 (http://hdl.handle.net/11858/00-097C-0000-0023-1AAF-3). The original train/dev/test sentence split of PDT3.0 corpus is preserved in this dataset.
Besides the data with artificial spelling errors, we also publish texts from which the character error model was created. These are the original manual transcript of an audiobook Švejk and its corrected version performed by authors of Korektor (http://ufal.mff.cuni.cz/korektor). These data are similarly to CzeSL Grammatical Error Correction Dataset (CzeSL-GEC: http://hdl.handle.net/11234/1-2143) processed into four sets based on error difficulty present.
CoNLL 2017 and 2018 shared tasks:
Multilingual Parsing from Raw Text to Universal Dependencies
This package contains the test data in the form in which they ware presented
to the participating systems: raw text files and files preprocessed by UDPipe.
The metadata.json files contain lists of files to process and to output;
README files in the respective folders describe the syntax of metadata.json.
For full training, development and gold standard test data, see
Universal Dependencies 2.0 (CoNLL 2017)
Universal Dependencies 2.2 (CoNLL 2018)
See the download links at http://universaldependencies.org/.
For more information on the shared tasks, see
http://universaldependencies.org/conll17/
http://universaldependencies.org/conll18/
Contents:
conll17-ud-test-2017-05-09 ... CoNLL 2017 test data
conll18-ud-test-2018-05-06 ... CoNLL 2018 test data
conll18-ud-test-2018-05-06-for-conll17 ... CoNLL 2018 test data with metadata
and filenames modified so that it is digestible by the 2017 systems.
Baseline UDPipe models for CoNLL 2017 Shared Task in UD Parsing, and supplementary material.
The models require UDPipe version at least 1.1 and are evaluated using the official evaluation script.
The models are trained on a slightly different split of the official UD 2.0 CoNLL 2017 training data, so called baselinemodel split, in order to allow comparison of models even during the shared task. This baselinemodel split of UD 2.0 CoNLL 2017 training data is available for download.
Furthermore, we also provide UD 2.0 CoNLL 2017 training data with automatically predicted morphology. We utilize the baseline models on development data and perform 10-fold jack-knifing (each fold is predicted with a model trained on the rest of the folds) on the training data.
Finally, we supply all required data and hyperparameter values needed to replicate the baseline models.
Baseline UDPipe models for CoNLL 2018 Shared Task in UD Parsing, and supplementary material.
The models require UDPipe version at least 1.2 and are evaluated using the official evaluation script. The models were trained using a custom data split for treebanks where no development data is provided. Also, we trained an additional "Mixed" model, which uses 200 sentences from every training data. All information needed to replicate the model training (hyperparameters, modified train-dev split, and pre-computed word embeddings for the parser) are included in the archive.
Additionaly, we provide UD 2.2 CoNLL 2018 training data with automatically predicted morphology. We utilize the baseline models on development data and perform 10-fold jack-knifing (each fold is predicted with a model trained on the rest of the folds) on the training data.
Corpus of texts in 12 languages. For each language, we provide one training, one development and one testing set acquired from Wikipedia articles. Moreover, each language dataset contains (substantially larger) training set collected from (general) Web texts. All sets, except for Wikipedia and Web training sets that can contain similar sentences, are disjoint. Data are segmented into sentences which are further word tokenized.
All data in the corpus contain diacritics. To strip diacritics from them, use Python script diacritization_stripping.py contained within attached stripping_diacritics.zip. This script has two modes. We generally recommend using method called uninames, which for some languages behaves better.
The code for training recurrent neural-network based model for diacritics restoration is located at https://github.com/arahusky/diacritics_restoration.
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!
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!
The Czech Legal Text Treebank 2.0 (CLTT 2.0) annotates the same texts as the CLTT 1.0. These texts come from the legal domain and they are manually syntactically annotated. The CLTT 2.0 annotation on the syntactic layer is more elaborate than in the CLTT 1.0 from various aspects. In addition, new annotation layers were added to the data: (i) the layer of accounting entities, and (ii) the layer of semantic entity relations.
The CzEngClass synonym verb lexicon is a result of a project investigating semantic ‘equivalence’ of verb senses and their valency behavior in parallel Czech-English language resources, i.e., relating verb meanings with respect to contextually-based verb synonymy. The lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (http://verbs.colorado.edu/verbnet/index.html), PropBank (http://verbs.colorado.edu/%7Empalmer/projects/ace.html), Ontonotes (http://verbs.colorado.edu/html_groupings/), and Czech (http://hdl.handle.net/11858/00-097C-0000-0001-4880-3) and English Wordnets (https://wordnet.princeton.edu/). Part of the dataset is a file reflecting annotators choices for assignment of verbs to classes.
The CzEngClass synonym verb lexicon is a result of a project investigating semantic ‘equivalence’ of verb senses and their valency behavior in parallel Czech-English language resources, i.e., relating verb meanings with respect to contextually-based verb synonymy. The lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (http://verbs.colorado.edu/verbnet/index.html), PropBank (http://verbs.colorado.edu/%7Empalmer/projects/ace.html), Ontonotes (http://verbs.colorado.edu/html_groupings/), and Czech (http://hdl.handle.net/11858/00-097C-0000-0001-4880-3) and English Wordnets (https://wordnet.princeton.edu/). Part of the dataset are files reflecting annotators choices and agreement for assignment of verbs to classes.
The CzEngClass synonym verb lexicon is a result of a project investigating semantic ‘equivalence’ of verb senses and their valency behavior in parallel Czech-English language resources, i.e., relating verb meanings with respect to contextually-based verb synonymy. The lexicon entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/fndrupal/), VerbNet (http://verbs.colorado.edu/verbnet/index.html), PropBank (http://verbs.colorado.edu/%7Empalmer/projects/ace.html), Ontonotes (http://verbs.colorado.edu/html_groupings/), and Czech (http://hdl.handle.net/11858/00-097C-0000-0001-4880-3) and English Wordnets (https://wordnet.princeton.edu/).
CzeSL-GEC is a corpus containing sentence pairs of original and corrected versions of Czech sentences collected from essays written by both non-native learners of Czech and Czech pupils with Romani background. To create this corpus, unreleased CzeSL-man corpus (http://utkl.ff.cuni.cz/learncorp/) was utilized. All sentences in the corpus are word tokenized.
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-2988). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
Deep Universal Dependencies is a collection of treebanks derived semi-automatically from Universal Dependencies (http://hdl.handle.net/11234/1-3105). It contains additional deep-syntactic and semantic annotations. Version of Deep UD corresponds to the version of UD it is based on. Note however that some UD treebanks have been omitted from Deep UD.
DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes (i.e. single lemmas, possibly with only a subset of their senses), edges represent derivational relations between a derived word and its base word. The present version, DeriNet 1.2, contains 1,003,590 lexemes (sampled from the MorfFlex dictionary) with 1,001,394 unique lemmas, connected by 740,750 derivational links. Both rather technical and linguistic changes were made as compared to the previous version of the data; e.g. new version of the MorfFlex dictionary was used, derived words that contain a consonant and/or vowel alternation (e.g. boží) were connected with their base word (bůh).
DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes, while edges represent derivational relations between a derived word and its base word. The present version, DeriNet 1.5, contains 1,011,965 lexemes (sampled from the MorfFlex dictionary) connected by 785,543 derivational links. Besides several rather conservative updates (such as newly identified prefix and suffix verb-to-verb derivations as well as noun-to-adjective derivations manifested by most frequent adjectival suffixes), DeriNet 1.5 is the first version that contains annotations related to compounding (compound words are distinguished by a special mark in their part-of-speech labels).
DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes, while edges represent derivational relations between a derived word and its base word. The present version, DeriNet 1.6, contains 1,027,832 lexemes (sampled from the MorfFlex dictionary) connected by 803,404 derivational links. Furthermore, starting with version 1.5, DeriNet contains annotations related to compounding (compound words are distinguished by a special mark in their part-of-speech labels).
Compared to version 1.5, version 1.6 was expanded by extracting potential links from dictionaries available under suitable licences, such as Wiktionary, and by enlarging the number of marked compounds.
DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes, while edges represent derivational or compositional relations between a derived word and its base word / words. The present version, DeriNet 2.0, contains 1,027,665 lexemes (sampled from the MorfFlex dictionary) connected by 808682 derivational and 600 compositional links.
Compared to previous versions, version 2.0 uses a new format and contains new types of annotations: compounding, annotation of several morphological and other categories of lexemes, identification of root morphs of 244,198 lexemes, semantic labelling of 151,005 relations using five labels and identification of 13 fictitious lexemes.
DeriNet is a lexical network which models derivational relations in the lexicon of Czech. Nodes of the network correspond to Czech lexemes, while edges represent word-formational relations between a derived word and its base word / words. The present version, DeriNet 2.1, contains 1,039,012 lexemes (sampled from the MorfFlex CZ 2.0 dictionary) connected by 782,814 derivational, 50,533 orthographic variant, 1,952 compounding, 295 univerbation and 144 conversion relations.
Compared to the previous version, version 2.1 contains annotations of orthographic variants, full automatically generated annotation of affix morpheme boundaries (in addition to the roots annotated in 2.0), 202 affixoid lexemes serving as bases for compounding, annotation of corpus frequency of lexemes, annotation of verbal conjugation classes and a pilot annotation of univerbation. The set of part-of-speech tags was converted to Universal POS from the Universal Dependencies project.
Extremely fast digital audio channelizer implementation, usable as a building block for experimental ASR front-ends or signal denoising applications. Also applicable in software defined radios, due to its high throughput. It comes in a form of a C/C++ library and an executable example program which reads input stream, splitting it into equidistant frequency channels, emitting their data to the output.
Features:
(1) Hand tuned SIMD-aware assembly for x86 (SSE) and IA64 (AVX) as well as for ARM (NEON) processors.
(2) Generic non-SIMD C++ implementation for other architectures.
(3) Capable of taking advantage of multicore CPUs.
(4) Fully configurable number of channels and the output decimation rate.
(5) User supplied FIR of the channel separation filter, which allows to specify the width of the channels, whether they should overlap or be separated.
(6) Input and output signal samples are treated as complex numbers.
(7) Speed over 750 complex MS/s achieved on Core i7 4710HQ @ 2.5GHz, when channelizing into 72 output channels with a FIR length of 1152 samples, using 3 computing threads.
(8) Runs under Linux OS.
English model for NameTag, a named entity recognition tool. The model is trained on CoNLL-2003 training data. Recognizes PER, ORG, LOC and MISC named entities. Achieves F-measure 84.73 on CoNLL-2003 test data.
Enriched discourse annotation of a subset of the Prague Discourse Treebank, adding implicit relations, entity based relations, question-answer relations and other discourse structuring phenomena.
Syntactic (including deep-syntactic - tectogrammatical) annotation of user-generated noisy sentences. The annotation was made on Czech-English and English-Czech Faust Dev/Test sets.
The English data includes manual annotations of English reference translations of Czech source texts. This texts were translated independently by two translators. After some necessary cleanings, 1000 segments were randomly selected for manual annotation. Both the reference translations were annotated, which means 2000 annotated segments in total.
The Czech data includes manual annotations of Czech reference translations of English source texts. This texts were translated independently by three translators. After some necessary cleanings, 1000 segments were randomly selected for manual annotation. All three reference translations were annotated, which means 3000 annotated segments in total.
Faust is part of PDT-C 1.0 (http://hdl.handle.net/11234/1-3185).
Data
----
Hindi Visual Genome 1.0, a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account. The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
Additionally, a challenge test set of 1400 segments will be released for the WAT2019 multi-modal task. This challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity.
Dataset Formats
--------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Hindi Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Segments English Words Hindi Words
------- --------- ---------------- -------------
Train 28932 143178 136722
Dev 998 4922 4695
Test 1595 7852 7535
Challenge Test 1400 8185 8665 (Released separately)
------- --------- ---------------- -------------
Total 32925 164137 157617
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019,
title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}},
author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan},
journal={Computaci{\'o}n y Sistemas},
note={In print. Presented at CICLing 2019, La Rochelle, France},
year={2019},
}
Indonesian text corpus from web. Crawling done by SpiderLing in 2017. Filtering by JusText and Onion (see http://corpus.tools/ for details). Tagged and lemmatized by MorphInd (http://septinalarasati.com/morphind/).
This package contains data sets for development and testing of machine translation of medical queries between Czech, English, French, German, Hungarian, Polish, Spanish ans Swedish. The queries come from general public and medical experts. This is version 2.0 extending the previous version by adding Hungarian, Polish, Spanish, and Swedish translations.
This package contains data sets for development (Section dev) and testing (Section test) of machine translation of sentences from summaries of medical articles between Czech, English, French, German, Hungarian, Polish, Spanish
and Swedish. Version 2.0 extends the previous version by adding Hungarian, Polish, Spanish, and Swedish translations.
This data set contains four types of manual annotation of translation quality, focusing on the comparison of human and machine translation quality (aka human-parity). The machine translation system used is English-Czech CUNI Transformer (CUBBITT). The annotations distinguish adequacy, fluency and overall quality. One of the types is Translation Turing test - detecting whether the annotators can distinguish human from machine translation.
All the sentences are taken from the English-Czech test set newstest2018 (WMT2018 News translation shared task www.statmt.org/wmt18/translation-task.html), but only from the half with originally English sentences translated to Czech by a professional agency.
MorfFlex CZ 2.0 is the Czech morphological dictionary developed originally by Jan Hajič as a spelling checker and lemmatization dictionary. MorfFlex is a flat list of lemma-tag-wordform triples. For each wordform, full inflectional information is coded in a positional tag. Wordforms are organized into entries (paradigm instances or paradigms in short) according to their formal morphological behavior. The paradigm (set of wordforms) is identified by a unique lemma. Apart from traditional morphological categories, the description also contains some semantic, stylistic and derivational information. For more details see a comprehensive specification of the Czech morphological annotation http://ufal.mff.cuni.cz/techrep/tr64.pdf .
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.
NER models for NameTag 2, named entity recognition tool, for English, German, Dutch, Spanish and Czech. Model documentation including performance can be found here: https://ufal.mff.cuni.cz/nametag/2/models . These models are for NameTag 2, named entity recognition tool, which can be found here: https://ufal.mff.cuni.cz/nametag/2 .
The NomVallex I. lexicon describes valency of Czech deverbal nouns belonging to three semantic classes, i.e. Communication (dotaz 'question'), Mental Action (plán 'plan') and Psych State (nenávist 'hatred'). It covers both stem-nominals and root-nominals (dotazování se 'asking' and dotaz 'question'). In total, the lexicon includes 505 lexical units in 248 lexemes. Valency properties are captured in the form of valency frames, specifying valency slots and their morphemic forms, and are exemplified by corpus examples.
In order to facilitate comparison, this submission also contains abbreviated entries of the source verbs of these nouns from the Vallex lexicon and simplified entries of the covered nouns from the PDT-Vallex lexicon.
Data
----
We have collected English-Odia parallel and monolingual data from the
available public websites for NLP research in Odia.
The parallel corpus consists of English-Odia parallel Bible, Odia
digital library, and Odisha Goverment websites. It covers bible,
literature, goverment of Odisha and its policies. We have processed the
raw data collected from the websites, performed alignments (a mix of
manual and automatic alignments) and release the corpus in a form ready
for various NLP tasks.
The Odia monolingual data consists of Odia-Wikipedia and Odia e-magazine
websites. Because the major portion of data is extracted from
Odia-Wikipedia, it covers all kinds of domains. The e-magazines data
mostly cover the literature domain. We have preprocessed the monolingual
data including de-duplication, text normalization, and sentence
segmentation to make it ready for various NLP tasks.
Corpus Formats
--------------
Both corpora are in simple tab-delimited plain text files.
The parallel corpus files have three columns:
- the original book/source of the sentence pair
- the English sentence
- the corresponding Odia sentence
The monolingual corpus has a varying number of columns:
- each line corresponds to one *paragraph* (or related unit) of the
original source
- each tab-delimited unit corresponds to one *sentence* in the paragraph
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Sentences #English tokens #Odia tokens
------- --------- ---------------- -------------
Train 27136 706567 604147
Dev 948 21912 19513
Test 1262 28488 24365
------- --------- ---------------- -------------
Total 29346 756967 648025
Domain Level Statistics
------------------------
Domain Sentences #English tokens #Odia tokens
------------------ --------- ---------------- -------------
Bible 29069 756861 640157
Literature 424 7977 6611
Goverment policies 204 1411 1257
------------------ --------- ---------------- -------------
Total 29697 766249 648025
Monolingual Corpus Statistics
-----------------------------
Paragraphs Sentences #Odia tokens
---------- --------- ------------
71698 221546 2641308
Domain Level Statistics
-----------------------
Domain Paragraphs Sentences #Odia tokens
-------------- -------------- --------- -------------
General (wiki) 30468 (42.49%) 102085 1320367
Literature 41230 (57.50%) 119461 1320941
-------------- -------------- --------- -------------
Total 71698 221546 2641308
Citation
--------
If you use this corpus, please cite it directly (see above), but please cite also the following paper:
Title: OdiEnCorp: Odia-English and Odia-Only Corpus for Machine Translation
Author: Shantipriya Parida, Ondrej Bojar, and Satya Ranjan Dash
Proceedings of the Third International Conference on Smart Computing & Informatics (SCI) 2018
Series: Smart Innovation, Systems and Technologies (SIST)
Publisher: Springer Singapore
ParaDi 2.0. is a dictionary of single verb paraphrases of Czech verbal multiword expressions - light verb constructions and idiomatic verb constructions. Moreover, it provides an elaborated set of morphological, syntactic and semantic features, including information on aspectual counterparts of verbs or paraphrasability conditions of given verbs.
The format of ParaDi has been designed with respect to both human and machine readability - the dictionary is represented as a plain table in TSV format, as it is a flexible and language-independent data format.
The valency lexicon PDT-Vallex 4.0 has been built in close connection with the annotation of the Prague Dependency Treebank project (PDT) and its successors (mainly the Prague Czech-English Dependency Treebank project, PCEDT, the spoken language corpus (PDTSC) and corpus of user-generated texts in the project Faust). It contains over 14500 valency frames for almost 8500 verbs which occurred in the PDT, PCEDT, PDTSC and Faust corpora. In addition, there are nouns, adjectives and adverbs, linked from the PDT part only, increasing the total to over 17000 valency frames for 13000 words. All the corpora have been published in 2020 as the PDT-C 1.0 corpus with the PDT-Vallex 4.0 dictionary included; this is a copy of the dictionary published as a separate item for those not interested in the corpora themselves. It is available in electronically processable format (XML), and also in more human readable form including corpus examples (see the WEBSITE link below, and the links to its main publications elsewhere in this metadata). The main feature of the lexicon is its linking to the annotated corpora - each occurrence of each verb is linked to the appropriate valency frame with additional (generalized) information about its usage and surface morphosyntactic form alternatives. It replaces the previously published unversioned edition of PDT-Vallex from 2014.
Prague Czech-English Dependency Treebank - Russian translation (PCEDT-R) is a project of translating a subset of Prague Czech-English Dependency Treebank 2.0 (PCEDT 2.0) to Russian and linguistically annotating the Russian translations with emphasis on coreference and cross-lingual alignment of coreferential expressions. Cross-lingual comparison of coreference means is currently the purpose that drives development of this corpus.
The current version 0.5 is a preliminary version, which contains (+ denotes new features):
* complete PCEDT 2.0 documents "wsj_1900"-"wsj_1949"
* Czech-English word alignment of coreferential expressions annotated manually mainly on the t-layer
+ Russian translations of the original English sentences
+ automatic tokenization, part-of-speech tagging and morphological analysis for Russian
+ automatic word alignment between all Czech and Russian words
+ manual alignment between Russian and the other two languages on possessive pronouns
The Prague Czech-English Dependency Treebank 2.0 Coref (PCEDT 2.0 Coref) is a parallel treebank building upon the original PCEDT 2.0 release and enriching it with the extended manual annotation of coreference, as well as with an improved automatic annotation of the coreferential expression alignment.
A richly annotated and genre-diversified language resource, The Prague Dependency Treebank – Consolidated 1.0 (PDT-C 1.0, or PDT-C in short in the sequel) is a consolidated release of the existing PDT-corpora of Czech data, uniformly annotated using the standard PDT scheme. PDT-corpora included in PDT-C: Prague Dependency Treebank (the original PDT contents, written newspaper and journal texts from three genres); Czech part of Prague Czech-English Dependency Treebank (translated financial texts, from English), Prague Dependency Treebank of Spoken Czech (spoken data, including audio and transcripts and multiple speech reconstruction annotation); PDT-Faust (user-generated texts). The difference from the separately published original treebanks can be briefly described as follows: it is published in one package, to allow easier data handling for all the datasets; the data is enhanced with a manual linguistic annotation at the morphological layer and new version of morphological dictionary is enclosed; a common valency lexicon for all four original parts is enclosed. Documentation provides two browsing and editing desktop tools (TrEd and MEd) and the corpus is also available online for searching using PML-TQ.
The Prague Dependency Treebank of Spoken Czech 2.0 (PDTSC 2.0) is a corpus of spoken language, consisting of 742,316 tokens and 73,835 sentences, representing 7,324 minutes (over 120 hours) of spontaneous dialogs. The dialogs have been recorded, transcribed and edited in several interlinked layers: audio recordings, automatic and manual transcripts and manually reconstructed text. These layers were part of the first version of the corpus (PDTSC 1.0). Version 2.0 is extended by an automatic dependency parser at the analytical and by the manual annotation of “deep” syntax at the tectogrammatical layer, which contains semantic roles and relations as well as annotation of coreference.
PDiT 2.0 is a new version of the Prague Discourse Treebank. It contains a complex annotation of discourse phenomena enriched by the annotation of secondary connectives.
The data contains the morphemic dictionary scanned in the PDF format. It is divided into 3 parts:
introductions.pdf - pp. 11-102
main_dictionary.pdf - pp. 113-506
appendices.pdf - pp. 509-645
The file contains all Czech verbs included in the Retrograde Morphemic Dictionary of Czech Language (Slavíčková Eleonora, Academia 1975).
The data was obtained by scanning a portion of the dictionary that contains words ending in -ci and -ti. Among them, there were 18 non-verbs, which were removed. Using OCR, the data was converted into the plain text format and the result was checked by two independent readers. However, if a user encounters a forgotten error, please report.
Trained models for UDPipe used to produce our final submission to the Vardial 2017 CLP shared task (https://bitbucket.org/hy-crossNLP/vardial2017). The SK model was trained on CS data, the HR model on SL data, and the SV model on a concatenation of DA and NO data. The scripts and commands used to create the models are part of separate submission (http://hdl.handle.net/11234/1-1970).
The models were trained with UDPipe version 3e65d69 from 3rd Jan 2017, obtained from
https://github.com/ufal/udpipe -- their functionality with newer or older versions of UDPipe is not guaranteed.
We list here the Bash command sequences that can be used to reproduce our results submitted to VarDial 2017. The input files must be in CoNLLU format. The models only use the form, UPOS, and Universal Features fields (SK only uses the form). You must have UDPipe installed. The feats2FEAT.py script, which prunes the universal features, is bundled with this submission.
SK -- tag and parse with the model:
udpipe --tag --parse sk-translex.v2.norm.feats07.w2v.trainonpred.udpipe sk-ud-predPoS-test.conllu
A slightly better after-deadline model (sk-translex.v2.norm.Case-feats07.w2v.trainonpred.udpipe), which we mention in the accompanying paper, is also included. It is applied in the same way (udpipe --tag --parse sk-translex.v2.norm.Case-feats07.w2v.trainonpred.udpipe sk-ud-predPoS-test.conllu).
HR -- prune the Features to keep only Case and parse with the model:
python3 feats2FEAT.py Case < hr-ud-predPoS-test.conllu | udpipe --parse hr-translex.v2.norm.Case.w2v.trainonpred.udpipe
NO -- put the UPOS annotation aside, tag Features with the model, merge with the left-aside UPOS annotation, and parse with the model (this hassle is because UDPipe cannot be told to keep UPOS and only change Features):
cut -f1-4 no-ud-predPoS-test.conllu > tmp
udpipe --tag no-translex.v2.norm.tgttagupos.srctagfeats.Case.w2v.udpipe no-ud-predPoS-test.conllu | cut -f5- | paste tmp - | sed 's/^\t$//' | udpipe --parse no-translex.v2.norm.tgttagupos.srctagfeats.Case.w2v.udpipe
Tools and scripts used to create the cross-lingual parsing models submitted to VarDial 2017 shared task (https://bitbucket.org/hy-crossNLP/vardial2017), as described in the linked paper. The trained UDPipe models themselves are published in a separate submission (https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1971).
For each source (SS, e.g. sl) and target (TT, e.g. hr) language,
you need to add the following into this directory:
- treebanks (Universal Dependencies v1.4):
SS-ud-train.conllu
TT-ud-predPoS-dev.conllu
- parallel data (OpenSubtitles from Opus):
OpenSubtitles2016.SS-TT.SS
OpenSubtitles2016.SS-TT.TT
!!! If they are originally called ...TT-SS... instead of ...SS-TT...,
you need to symlink them (or move, or copy) !!!
- target tagging model
TT.tagger.udpipe
All of these can be obtained from https://bitbucket.org/hy-crossNLP/vardial2017
You also need to have:
- Bash
- Perl 5
- Python 3
- word2vec (https://code.google.com/archive/p/word2vec/); we used rev 41 from 15th Sep 2014
- udpipe (https://github.com/ufal/udpipe); we used commit 3e65d69 from 3rd Jan 2017
- Treex (https://github.com/ufal/treex); we used commit d27ee8a from 21st Dec 2016
The most basic setup is the sl-hr one (train_sl-hr.sh):
- normalization of deprels
- 1:1 word-alignment of parallel data with Monolingual Greedy Aligner
- simple word-by-word translation of source treebank
- pre-training of target word embeddings
- simplification of morpho feats (use only Case)
- and finally, training and evaluating the parser
Both da+sv-no (train_ds-no.sh) and cs-sk (train_cs-sk.sh) add some cross-tagging, which seems to be useful only in
specific cases (see paper for details).
Moreover, cs-sk also adds more morpho features, selecting those that
seem to be very often shared in parallel data.
The whole pipeline takes tens of hours to run, and uses several GB of RAM, so make sure to use a powerful computer.
Slovak models for MorphoDiTa, providing morphological analysis, morphological generation and part-of-speech tagging.
The morphological dictionary is created from MorfFlex SK 170914 and the PoS tagger is trained on automatically translated Prague Dependency Treebank 3.0 (PDT).
Simple question answering database version 2.1 (SQAD_v2.1) created from Czech Wikipedia. Each record of SQAD consist of four files (in vertical form provided with lemmatization and POS tagging) and two metadata files.
Ministerstvo školství, mládeže a tělovýchovy České republiky@@LM2015071@@LINDAT/CLARIN: Institut pro analýzu, zpracování a distribuci lingvistických dat@@nationalFunds@@✖[remove]73