This corpus constitutes all sentences representing the Arabic Controlled Language (ACL). It contains 551 sentences taken from four textbooks and websites dedicated to teach Arabic language to kids such as: a) First grade book, Republic of Sudan (كتاب الصف الاول جمهورية السودان), b) Al Jazeera Educational Site (موقع الجزيرة التعليمي), c) Bella Preparatory School Girls Forum (منتدى مدرسة بيلا الاعدادية بنات), and d) Albahr website (موقع انا البحر). These sentences are respecting 52 ACL rules. The average number of sentences for each rule is 10.6. All sentences in the corpus were analyzed by Farasa syntactic parser to confirm they are correctly analyzed. The validity of the parsing was done manually by linguist experts.
The structure of this corpus is made of a header and a body. The header consists of a set of metadata that describe the corpus, such as the corpus name, the authors, the sources and further meta data. While the header is made of metadata, the body contains rules. Each rule has a code, a structure and all sentences respecting that rule. For each sentence, we store an id, the vowelledand unvowelled text as well as the result of parsing using Farasa.
Relationship extraction models for the Czech language. Models are trained on CERED (dataset created by distant supervision on Czech Wikipedia and Wikidata) and recognize a subset of Wikidata relations (listed in CEREDx.LABELS).
We supply a demo.py that performs inference on user-defined input and requirements.txt file for pip. Adapt the demo code to use the model.
Both the dataset and the models are presented in Relationship Extraction thesis.
Data
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Hindi Visual Genome 1.1 is an updated version of Hindi Visual Genome 1.0. The update concerns primarily the text part of Hindi Visual Genome, fixing translation issues reported during WAT 2019 multimodal task. In the image part, only one segment and thus one image were removed from the dataset.
Hindi Visual Genome 1.1 serves in "WAT 2020 Multi-Modal Machine Translation Task".
Hindi Visual Genome is 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.
A third test set is called ``challenge test set'' consists of 1.4K segments and it was released for WAT2019 multi-modal task. The 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. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word.
Dataset Formats
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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
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The statistics of the current release is given below.
Parallel Corpus Statistics
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Dataset Segments English Words Hindi Words
------- --------- ---------------- -------------
Train 28930 143164 145448
Dev 998 4922 4978
Test 1595 7853 7852
Challenge Test 1400 8186 8639
------- --------- ---------------- -------------
Total 32923 164125 166917
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},
volume={23},
number={4},
pages={1499--1505},
year={2019}
}
The Dictionary of Medieval Latin in the Czech Lands registers and explains the vocabulary of Medieval Latin as used in the Czech lands since the beginnings of Latin writing in this area (from about 1000 CE) to 1500 CE, so far covering the letters A-M. For more information about the Dictionary, see the webpage of the Department of Medieval Lexicography of the Institute of Philosophy of Czech Academy of Sciences.
The data uploaded present the on-line version of the dictionary (API and XML data), making it possible to put the application into operation at a localhost.
Model trained for Czech POS Tagging and Lemmatization using Czech version of BERT model, RobeCzech. Model is trained on data from Prague Dependency Treebank 3.5. Model is a part of Czech NLP with Contextualized Embeddings master thesis and presented a state-of-the-art performance on the date of submission of the work.
Demo jupyter notebook is available on the project GitHub.
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 presented game is designed to teach the six most frequent English prepositions (to, of, in, for, on, and with) at the A1 to A2 levels of proficiency. Prep for Adventure is a single-player game comprised of five separate tasks – jumping puzzle, cooking, town maze, lighting the goblets, and a banter with a classmate. Their mechanics are then combined in the final task (The Final Fight) to elicit the correct responses of the subject.
The language used in the game is adjusted for the subjects’ level of proficiency, the game is fully voiced and offers a degree of customization. All tasks are based on the gap-filling type of exercise where subjects have to complete a sentence with a missing word, either by typing it in or via different kinds of multiple-choice formats. The game is designed to advance the subjects’ performance in prepositional structures by exposing players to as much input as possible.
The length of one average playthrough is approximately 30-45 minutes. The game was created in the RPG Maker MV engine where RPG stands for role-playing game, which is a genre of a game in which the player adopts a role/roles of a fictional character/characters in a (partly or fully) invented setting.
The game story:
The Grammar School of Witchcraft has been taken over by the Evil Preposition Magician and the player is trying to win their school back alongside with a young witch named Morphologina (the player’s guide).
Language acquisition is one of the currently much discussed topics in the field of psycholinguistics. Considerable space for future research can be seen in the development of vocabulary in Czech-speaking children. In our case, we are mainly interested in the meaning, i.e. the content of acquired words (concepts), and the role of so-called semantic features in mental representation.
The intended goal of our research is to bring new information from the above-mentioned area, to confirm or disprove some existing theoretical statements and to compare the results of foreign research with data obtained using the Czech language material. Similar research has been conducted in various world languages, but so far there are not many papers that address the issue in the Czech language environment. As part of our work, a comprehensive database of semantic features for selected concepts has been prepared. This database has been statistically processed and subsequently the data has been analyzed and interpreted on the basis of theories about the development of the child's speech competence. This material, obtained from children aged 8-9 (lower primary school) growing up in a Czech language environment, has been used in the next phase of research, in which an experiment with subjects belonging to the same age category has been performed: in a semantic task based on the phenomenon called semantic priming, the effect of featural similarity of two concepts on decision in a speeded task has been observed.
The results of the research expand the range of information published so far in this scientific field in the Czech environment. This research can provide valuable insights into children's language acquisition issues. The data gathered can also be practically beneficial not only for teachers, psychologists and speech therapists, but also for parents, for example.
Sentiment analysis models for Czech language. Models are three Czech sentiment analysis datasets(http://liks.fav.zcu.cz/sentiment/): Mall, CSFD, Facebook, and joint data from all three datasets above, using Czech version of BERT model, RobeCzech.
We present the best model for every dataset. Mall and CSFD models are new state-of-the-art for respective data.
Demo jupyter notebook is available on the project GitHub.
These models are a part of Czech NLP with Contextualized Embeddings master thesis.