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602. Logos : multilingual e-translation portal
- Publisher:
- LOGOS
- Type:
- corpus
- Description:
- Searchable multilingual text collection (700+ mwd) and a dictionary database of 251 languages and dialects. The Dictionary (ca. 8 mwd) provides translation of a word, definition, grammar, synonym, antonym, image, pronunciation, etc.
- Rights:
- Not specified
603. LongEval Click-Model Relevance Judgements (Qrels)
- Creator:
- Galuščáková, Petra, Devaud, Romain, Gonzalez-Saez, Gabriela, Mulhem, Philippe, Goeuriot, Lorraine, Piroi, Florina, and Popel, Martin
- Publisher:
- Université Grenoble Alpes
- Type:
- text and corpus
- Subject:
- information retrieval, automatic evaluation, and evaluation
- Language:
- French and English
- Description:
- The collection comprises the relevance judgments used in the 2023 LongEval Information Retrieval Lab (https://clef-longeval.github.io/), organized at CLEF. It consists of three sets of relevance judgments: 1) Relevance judgments for the heldout queries from the LongEval Train Collection (http://hdl.handle.net/11234/1-5010). 2) Relevance judgments for the short-term persistence (sub-task A) queries from the LongEval Test Collection (http://hdl.handle.net/11234/1-5139). 3) Relevance judgments for the long-term persistence (sub-task B) queries from the LongEval Test Collection (http://hdl.handle.net/11234/1-5139). These judgments were provided by the Qwant search engine (https://www.qwant.com) and were generated using a click model. The click model output was based on the clicks of Qwant's users, but it mitigates noise from raw user clicks caused by positional bias and also better safeguards users' privacy. Consequently, it can serve as a reliable soft relevance estimate for evaluating and training models. The collection includes a total of 1,420 judgments for the heldout queries, with 74 considered highly relevant and 326 deemed relevant. For the short-term sub-task queries, there are 12,217 judgments, including 762 highly relevant and 2,608 relevant ones. As for the long-term sub-task queries, there are 13,467 judgments, with 936 being highly relevant and 2,899 relevant.
- Rights:
- Qwant LongEval Attribution-NonCommercial-ShareAlike License, https://lindat.mff.cuni.cz/repository/xmlui/page/Qwant_LongEval_BY-NC-SA_License, and PUB
604. LongEval Test Collection
- Creator:
- Galuščáková, Petra, Devaud, Romain, Gonzalez-Saez, Gabriela, Mulhem, Philippe, Goeuriot, Lorraine, Piroi, Florina, and Popel, Martin
- Publisher:
- Université Grenoble Alpes, Qwant, Research Studios Austria, and Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- information retrieval, cross-language, cross-lingual information retrieval, parallel corpus, and search
- Language:
- French and English
- Description:
- The collection consists of queries and documents provided by the Qwant search Engine (https://www.qwant.com). The queries, which were issued by the users of Qwant, are based on the selected trending topics. The documents in the collection are the webpages which were selected with respect to these queries using the Qwant click model. Apart from the documents selected using this model, the collection also contains randomly selected documents from the Qwant index. The collection serves as the official test collection for the 2023 LongEval Information Retrieval Lab (https://clef-longeval.github.io/) organised at CLEF. The collection contains test datasets for two organized sub-tasks: short-term persistence (sub-task A) and long-term persistence (sub-task B). The data for the short-term persistence sub-task was collected over July 2022 and this dataset contains 1,593,376 documents and 882 queries. The data for the long-term persistence sub-task was collected over September 2022 and this dataset consists of 1,081,334 documents and 923 queries. Apart from the original French versions of the webpages and queries, the collection also contains their translations into English.
- Rights:
- Qwant LongEval Attribution-NonCommercial-ShareAlike License, https://lindat.mff.cuni.cz/repository/xmlui/page/Qwant_LongEval_BY-NC-SA_License, and PUB
605. LongEval Train Collection
- Creator:
- Galuščáková, Petra, Devaud, Romain, Gonzalez-Saez, Gabriela, Mulhem, Philippe, Goeuriot, Lorraine, Piroi, Florina, and Popel, Martin
- Publisher:
- Université Grenoble Alpes, Qwant, Research Studios Austria, and Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- information retrieval, parallel corpus, search, and automatic evaluation
- Language:
- French and English
- Description:
- The collection consists of queries and documents provided by the Qwant search Engine (https://www.qwant.com). The queries, which were issued by the users of Qwant, are based on the selected trending topics. The documents in the collection were selected with respect to these queries using the Qwant click model. Apart from the documents selected using this model, the collection also contains randomly selected documents from the Qwant index. All the data were collected over June 2022. In total, the collection contains 672 train queries, with corresponding 9656 assessments coming from the Qwant click model, and 98 heldout queries. The set of documents consist of 1,570,734 downloaded, cleaned and filtered Web Pages. Apart from their original French versions, the collection also contains translations of the webpages and queries into English. The collection serves as the official training collection for the 2023 LongEval Information Retrieval Lab (https://clef-longeval.github.io/) organised at CLEF.
- Rights:
- Qwant LongEval Attribution-NonCommercial-ShareAlike License, PUB, and https://lindat.mff.cuni.cz/repository/xmlui/page/Qwant_LongEval_BY-NC-SA_License
606. Machine Translation Testsuite for Gender-Consistent Translation
- Creator:
- Aires, João Paulo
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- text and corpus
- Subject:
- machine translation, testsuite, evaluation, and gender
- Language:
- English and Czech
- Description:
- Document-level testsuite for evaluation of gender translation consistency. Our Document-Level test set consists of selected English documents from the WMT21 newstest annotated with gender information. Czech unnanotated references are also added for convenience. We semi-automatically annotated person names and pronouns to identify the gender of these elements as well as coreferences. Our proposed annotation consists of three elements: (1) an ID, (2) an element class, and (3) gender. The ID identifies a person's name and its occurrences (name and pronouns). The element class identifies whether the tag refers to a name or a pronoun. Finally, the gender information defines whether the element is masculine or feminine. We performed a series of NLP techniques to automatically identify person names and coreferences. This initial process resulted in a set containing 45 documents to be manually annotated. Thus, we started a manual annotation of these documents to make sure they are correctly tagged. See README.md for more details.
- Rights:
- Creative Commons - Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), http://creativecommons.org/licenses/by-nc/4.0/, and PUB
607. MADED
- Creator:
- ridouane, tachicart and karim, bouzoubaa
- Publisher:
- ALELM
- Type:
- text, computationalLexicon, and lexicalConceptualResource
- Subject:
- lexicon and moroccan arabic
- Language:
- Moroccan Arabic and Arabic
- Description:
- Moroccan Dialect Electronic Dictionary (MDED) is an electronic lexicon containing almost 15000 MSA entries. They are written in Arabic letters and translated to Moroccan Arabic dialect. In addition, MDED entries are annotated useful metadata such as POS, Origin and root. MDED can be useful in some advanced NLP applications such as Machine translation and morphological analyzer.
- Rights:
- Creative Commons - Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), http://creativecommons.org/licenses/by-nc/4.0/, and PUB
608. Malach Center User Interface 1.0
- Creator:
- Kocián, Jiří and Obdržálek, Pavel
- Publisher:
- Malach Center for Visual History, Institute of Formal and Applied Linguistics, Charles University
- Type:
- tool and toolService
- Subject:
- digital humanities, database, search engine, Jews, Holocaust, History, Oral History, Visual History, and digital archive of cultural heritage
- Description:
- Source code of the first full and running version for the Malach Center User Interface, does not contain data or metadata fo the digital objects and resources.
- Rights:
- GNU General Public Licence, version 3, http://opensource.org/licenses/GPL-3.0, and PUB
609. Malayalam Visual Genome 1.0
- Creator:
- Parida, Shantipriya and Bojar, Ondřej
- Publisher:
- Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
- Type:
- image and corpus
- Subject:
- multi-modal, neural machine translation, English-Malayalam Multimodal Corpus, Malayalam Image Captioning, and English-Malayalam Multimodal Translation
- Language:
- English and Malayalam
- Description:
- Data ------- Malayalam Visual Genome (MVG for short) 1.0 has similar goals as Hindi Visual Genome (HVG) 1.1: to support the Malayalam language. Malayalam Visual Genome 1.0 is the first multi-modal dataset in Malayalam for machine translation and image captioning. Malayalam Visual Genome 1.0 serves in "WAT 2021 Multi-Modal Machine Translation Task". Malayalam Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Malayalam multimodal machine translation task and multimodal research. We follow the same selection of short English segments (captions) and the associated images from Visual Genome as HGV 1.1 has. For MVG, we automatically translated these captions from English to Malayalam and manually corrected them, taking the associated images into account. The training set contains 29K segments. Further 1K and 1.6K segments are provided in 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'' and consists of 1.4K segments. The challenge test set was created for the WAT2019 multi-modal task 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. For MVG, we simply translated the English side of the test sets to Malayalam, again utilizing machine translation to speed up the process. 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 - Malayalam 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 are given below. Parallel Corpus Statistics --------------------------------- Dataset Segments English Words Malayalam Words ---------- -------------- -------------------- ----------------- Train 28930 143112 107126 Dev 998 4922 3619 Test 1595 7853 5689 Challenge Test 1400 8186 6044 -------------------- ------------ ------------------ ------------------ Total 32923 164073 122478 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} }
- Rights:
- Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), http://creativecommons.org/licenses/by-nc-sa/4.0/, and PUB
610. maltitok
- Publisher:
- University of Malta
- Type:
- toolService
- Description:
- A tokeniser for the Maltese language. The tokeniser accepts UTF8 text and produces UTF8 text, so can be used in a pipeline.
- Rights:
- Not specified