The representative full-text digitalized HetWiK corpus is composed of 140 manually annotated texts of the German Resistance between 1933 and 1945. This includes both well-known and relatively unknown documents, public writings, like pamphlets or memoranda, as well as private texts, e.g. letters, journal or prison entries and biographies. Thus the corpus represents the diverse groups as well as the heterogeneity of verbal resistance and allows the study of resistance in relation to the language usage.
The HetWiK corpus can be used free of charge. A detailed register of the individual texts and further information about the tagset can be found on the project-homepage (german). In addition to the CATMA5 XML-format we provide a standoff-JSON format and CEC6-Files (CorpusExplorer) - so you can export the HetWiK corpus in different formats.
This text corpus contains a carefully optimized set of sentences that could be used in the process of preparing a speech corpus for the development of personalized text-to-speech system. It was designed primarily for the voice conservation procedure that must be performed in a relatively short period before a person loses his/her own voice, typically because of the total laryngectomy.
Total laryngectomy is a radical treatment procedure which is often unavoidable to save life of patients who were diagnosed with severe laryngeal cancer. In spite of being very effective with respect to the primary treatment, it significantly handicaps the patients due to the permanent loss of their ability to use voice and produce speech. Luckily, the modern methods of computer text-to-speech (TTS) synthesis offer a possibility for "digital conservation" of patient's original voice for his/her future speech communication -- a procedure called voice banking or voice conservation. Moreover, the banking procedure can be undertaken by any person facing voice degradation or loss in farther future, or who is simply is willing to keep his/her voice-print.
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
--------------
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}
}
HinDialect: 26 Hindi-related languages and dialects of the Indic Continuum in North India
Languages
This is a collection of folksongs for 26 languages that form a dialect continuum in North India and nearby regions.
Namely Angika, Awadhi, Baiga, Bengali, Bhadrawahi, Bhili, Bhojpuri, Braj, Bundeli, Chhattisgarhi, Garhwali, Gujarati, Haryanvi, Himachali, Hindi, Kanauji, Khadi Boli, Korku, Kumaoni, Magahi, Malvi, Marathi, Nimadi, Panjabi, Rajasthani, Sanskrit.
This data is originally collected by the Kavita Kosh Project at http://www.kavitakosh.org/ . Here are the main characteristics of the languages in this collection:
- They are all Indic languages except for Korku.
- The majority of them are closely related to the standard Hindi dialect genealogically (such as Hariyanvi and Bhojpuri), although the collection also contains languages such as Bengali and Gujarati which are more distant relatives.
- They are all primarily spoken in (North) India (Bengali is also spoken in Bangladesh)
- All except Sanksrit are alive languages
Data
Categorising them by pre-existing available NLP resources, we have:
* Band 1 languages : Hindi, Panjabi, Gujarati, Bengali, Nepali. These languages already have other large standard datasets available. Kavita Kosh may have very little data for these languages.
* Band 2 languages: Bhojpuri, Magahi, Awadhi, Braj. These languages have growing interest and some datasets of a relatively small size as compared to Band 1 language resources.
* Band 3 languages: All other languages in the collection are previously zero-resource languages. These are the languages for which this dataset is the most relevant.
Script
This dataset is entirely in Devanagari. Content in the case of languages not written in Devanagari (such as Bengali and Gujarati) has been transliterated by the Kavita Kosh Project.
Format
The dataset contains a single text file containing folksongs per language. Folksongs are separated from each other by an empty line. The first line of a new piece is the title of the folksong, and line separation within folksongs is preserved.
HinDialect: 26 Hindi-related languages and dialects of the Indic Continuum in North India
Languages
This is a collection of folksongs for 26 languages that form a dialect continuum in North India and nearby regions.
Namely Angika, Awadhi, Baiga, Bengali, Bhadrawahi, Bhili, Bhojpuri, Braj, Bundeli, Chhattisgarhi, Garhwali, Gujarati, Haryanvi, Himachali, Hindi, Kanauji, Khadi Boli, Korku, Kumaoni, Magahi, Malvi, Marathi, Nimadi, Panjabi, Rajasthani, Sanskrit.
This data is originally collected by the Kavita Kosh Project at http://www.kavitakosh.org/ . Here are the main characteristics of the languages in this collection:
- They are all Indic languages except for Korku.
- The majority of them are closely related to the standard Hindi dialect genealogically (such as Hariyanvi and Bhojpuri), although the collection also contains languages such as Bengali and Gujarati which are more distant relatives.
- All except Nepali are primarily spoken in (North) India
- All except Sanksrit are alive languages
Data
Categorising them by pre-existing available NLP resources, we have:
* Band 1 languages : Hindi, Marathi, Punjabi, Sindhi, Gujarati, Bengali, Nepali. These languages already have other large datasets available. Since Kavita Kosh focusses largely on Hindi-related languages, we may have very little data for these other languages in this particular dataset.
* Band 2 languages: Bhojpuri, Magahi, Awadhi, Brajbhasha. These languages have growing interest and some datasets of a relatively small size as compared to Band 1 language resources.
* Band 3 languages: All other languages in the collection are previously zero-resource languages. These are the languages for which this dataset is the most relevant.
Script
This dataset is entirely in Devanagari. Content in the case of languages not written in Devanagari (such as Bengali and Gujarati) has been transliterated by the Kavita Kosh Project.
Format
The data is segregated by language, and contains each folksong in a different JSON file.
The book [1] contains spelling rules classified into ten categories, each category containing many rules. This XML file presents our implemented rules classified with six category tags, as is the case in the book. We implemented 24 rules since the remaining rules require diacritical and morphological analysis that are outside the scope of our present work.
References:
[1] Dr.Fahmy Al-Najjar, 'Spelling rules in ten easy lessons', Al Kawthar Library,2008. Available: https://www.alukah.net/library/0/53498/%D9%82%D9%88%D8%A7%D8%B9%D8%AF-%D8%A7%D9%84%D8%A5%D9%85%D9%84%D8%A7%D8%A1-%D9%81%D9%8A-%D8%B9%D8%B4%D8%B1%D8%A9-%D8%AF%D8%B1%D9%88%D8%B3-%D8%B3%D9%87%D9%84%D8%A9-pdf/
This corpus consists of full transcriptions of both Democratic and Republican 2016 presidential candidate debates, with a special focus on the idiolects of Hillary Clinton and Donald Trump against the background of the speeches of other candidates for the post of president of the United States.
The transcriptions are sourced from the American Presidency Project at the University of California, Santa Barbara. Any use of the material requires a prior and explicit written permission by the project administrator (contact policy@ucsb.edu). This corpus material is now being shared with their kindly permission.
This resource is an Italian morphological dictionary for content words, encoded in a JSON Lines format text file. It contains correspondences between surface form and lexical forms of words followed by grammatical features. The surface word forms have been generated algorithmically by using stable phonological and morphological rules of the Italian language. Particular attention has been given to the generation of verbs for which rules have been extracted from the famous A.L e G. Lepschy, La lingua italiana. The dictionary with its remarkable coverage is particularly useful used together with the Italian Function Words (http://hdl.handle.net/11372/LRT-2288) for tasks such as POS-Tagging or Syntactic Parsing.
This resource is the second version of an Italian morphological dictionary for content words, encoded in a JSON Lines format text file. It contains correspondences between surface form and lexical forms of words followed by standard grammatical properties. Compared to the first release, this version has a better JSON structure. The surface word forms have been generated algorithmically by using stable phonological and morphological rules of the Italian language. Particular attention has been given to the generation of verbs for which rules have been extracted from A.L e G. Lepschy, La Lingua Italiana. The dictionary with its remarkable coverage is particularly useful used together with the Italian Function Words v2 (http://hdl.handle.net/11372/LRT-2629) for tasks such as pos-tagging or syntactic parsing.
This resource is the third version of the Italian morphological dictionary for content words (http://hdl.handle.net/11372/LRT-2630), encoded in a JSON Lines format. Compared to the previous version, it contains some minor improvements.