Turkish-English parallel corpus MaCoCu-tr-en 2.0

Name: Turkish-English parallel corpus MaCoCu-tr-en 2.0
License: https://creativecommons.org/publicdomain/zero/1.0/

Bañón, Marta; Chichirau, Malina; Esplà-Gomis, Miquel; Forcada, Mikel L.; Galiano-Jiménez, Aarón; García-Romero, Cristian; Kuzman, Taja; Ljubešić, Nikola; van Noord, Rik; Pla Sempere, Leopoldo; Ramírez-Sánchez, Gema; Rupnik, Peter; Suchomel, Vít; Toral, Antonio; Zaragoza-Bernabeu, Jaume

Turkish-English parallel corpus MaCoCu-tr-en 2.0

CLARIN.SI data & tools

Authors: Bañón, Marta ; et al.show everyone
Bañón, Marta ; Chichirau, Malina ; Esplà-Gomis, Miquel ; Forcada, Mikel L. ; Galiano-Jiménez, Aarón ; García-Romero, Cristian ; Kuzman, Taja ; Ljubešić, Nikola ; van Noord, Rik ; Pla Sempere, Leopoldo ; Ramírez-Sánchez, Gema ; Rupnik, Peter ; Suchomel, Vít ; Toral, Antonio ; Zaragoza-Bernabeu, Jaume

Item identifier: http://hdl.handle.net/11356/1816

Project URL: https://macocu.eu/

Referenced by: https://hdl.handle.net/11370/685514a8-947e-44f9-83cf-90356c5f1684

Date issued: 2023-04-26

Type: corpus, text

Size: 1646739 entries, 89231511 words, 584752 texts

Language(s): English , Turkish

Description: The Turkish-English parallel corpus MaCoCu-tr-en 2.0 was built by crawling the “.tr” and “.cy” internet top-level domains in 2021, extending the crawl dynamically to other domains as well. All the crawling process was carried out by the MaCoCu crawler (https://github.com/macocu/MaCoCu-crawler). Websites containing documents in both target languages were identified and processed using the tool Bitextor (https://github.com/bitextor/bitextor). Considerable effort was devoted into cleaning the extracted text to provide a high-quality parallel corpus. This was achieved by removing boilerplate and near-duplicated paragraphs and documents that are not in one of the targeted languages. Document and segment alignment as implemented in Bitextor were carried out, and Bifixer (https://github.com/bitextor/bifixer) and BicleanerAI (https://github.com/bitextor/bicleaner-ai) were used for fixing, cleaning, and deduplicating the final version of the corpus. The corpus is available in three formats: two sentence-level formats, TXT and TMX, and a document-level TXT format. TMX is an XML-based format and TXT is a tab-separated format. They both consist of pairs of source and target segments (one or several sentences) and additional metadata. The following metadata is included in both sentence-level formats: - source and target document URL; - paragraph ID which includes information on the position of the sentence in the paragraph and in the document (e.g., “p35:77s1/3” which means “paragraph 35 out of 77, sentence 1 out of 3”); - quality score as provided by the tool Bicleaner AI (a likelihood of a pair of sentences being mutual translations, provided with a score between 0 and 1); - similarity score as provided by the sentence alignment tool Bleualign (value between 0 and 1); - personal information identification (“biroamer-entities-detected”): segments containing personal information are flagged, so final users of the corpus can decide whether to use these segments; - translation direction and machine translation identification (“translation-direction”): the source segment in each segment pair was identified by using a probabilistic model (https://github.com/RikVN/TranslationDirection), which also determines if the translation has been produced by a machine-translation system; - a DSI class (“dsi”): information whether the segment is connected to any of Digital Service Infrastructure (DSI) classes (e.g., cybersecurity, e-health, e-justice, open-data-portal), defined by the Connecting Europe Facility (https://github.com/RikVN/DSI); - English language variant: the language variant of English (British or American, using a lexicon-based English variety classifier - https://pypi.org/project/abclf/) was identified on document and domain level. Furthermore, the sentence-level TXT format provides additional metadata: - web domain of the text; - source and target document title; - the date when the original file was retrieved; - the original type of the file (e.g., “html”), from which the sentence was extracted; - paragraph quality (labels, such as “short” or “good”, assigned based on paragraph length, URL and stopword density via the jusText tool - https://corpus.tools/wiki/Justext); - information whether the sentence is a heading or not in the original document. The document-level TXT format provides pairs of documents identified to contain parallel data. In addition to the parallel documents (in base64 format), the corpus includes the following metadata: source and target document URL, a DSI category and the English language variant (British or American). As opposed to the previous version, this version has more accurate metadata on languages of the texts, which was achieved by using Google's Compact Language Detector 2 (CLD2) (https://github.com/CLD2Owners/cld2), a high-performance language detector supporting many languages. Other tools, used for web corpora creation and curation, have been updated as well, resulting in an even cleaner corpus. The new version also provides additional metadata, such as the position of the sentence in the paragraph and document, and information whether the sentence is related to a DSI. Moreover, the corpus is now also provided in a document-level format. Notice and take down: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: (1) Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. (2) Clearly identify the copyrighted work claimed to be infringed. (3) Clearly identify the material that is claimed to be infringing and information reasonably sufficient in order to allow us to locate the material. (4) Please write to the contact person for this resource whose email is available in the full item record. We will comply with legitimate requests by removing the affected sources from the next release of the corpus. This action has received funding from the European Union's Connecting Europe Facility 2014-2020 - CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341. This communication reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains.