Why the World Reads Wikipedia: Beyond English Speakers
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A Survey of Orthographic Information in Machine Translation 3
Machine Translation Journal manuscript No. (will be inserted by the editor) A Survey of Orthographic Information in Machine Translation Bharathi Raja Chakravarthi1 ⋅ Priya Rani 1 ⋅ Mihael Arcan2 ⋅ John P. McCrae 1 the date of receipt and acceptance should be inserted later Abstract Machine translation is one of the applications of natural language process- ing which has been explored in different languages. Recently researchers started pay- ing attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine transla- tion systems is the variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable, but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthog- raphy’s influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic in- formation can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under- resourced languages. We discuss different types of machine translation and demon- strate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts of cog- nates information at different levels of machine translation and the lessons that can Bharathi Raja Chakravarthi [email protected] Priya Rani [email protected] Mihael Arcan [email protected] John P. -
State of Wikimedia Communities of India
State of Wikimedia Communities of India Assamese http://as.wikipedia.org State of Assamese Wikipedia RISE OF ASSAMESE WIKIPEDIA Number of edits and internal links EDITS PER MONTH INTERNAL LINKS GROWTH OF ASSAMESE WIKIPEDIA Number of good Date Articles January 2010 263 December 2012 301 (around 3 articles per month) November 2011 742 (around 40 articles per month) Future Plans Awareness Sessions and Wiki Academy Workshops in Universities of Assam. Conduct Assamese Editing Workshops to groom writers to write in Assamese. Future Plans Awareness Sessions and Wiki Academy Workshops in Universities of Assam. Conduct Assamese Editing Workshops to groom writers to write in Assamese. THANK YOU Bengali বাংলা উইকিপিডিয়া Bengali Wikipedia http://bn.wikipedia.org/ By Bengali Wikipedia community Bengali Language • 6th most spoken language • 230 million speakers Bengali Language • National language of Bangladesh • Official language of India • Official language in Sierra Leone Bengali Wikipedia • Started in 2004 • 22,000 articles • 2,500 page views per month • 150 active editors Bengali Wikipedia • Monthly meet ups • W10 anniversary • Women’s Wikipedia workshop Wikimedia Bangladesh local chapter approved in 2011 by Wikimedia Foundation English State of WikiProject India on ENGLISH WIKIPEDIA ● One of the largest Indian Wikipedias. ● WikiProject started on 11 July 2006 by GaneshK, an NRI. ● Number of article:89,874 articles. (Excludes those that are not tagged with the WikiProject banner) ● Editors – 465 (active) ● Featured content : FAs - 55, FLs - 20, A class – 2, GAs – 163. BASIC STATISTICS ● B class – 1188 ● C class – 801 ● Start – 10,931 ● Stub – 43,666 ● Unassessed for quality – 20,875 ● Unknown importance – 61,061 ● Cleanup tags – 43,080 articles & 71,415 tags BASIC STATISTICS ● Diversity of opinion ● Lack of reliable sources ● Indic sources „lost in translation“ ● Editor skills need to be upgraded ● Lack of leadership ● Lack of coordinated activities ● …. -
As You Wish Meaning in Bengali
As You Wish Meaning In Bengali Collapsable Jack stet glumly and stockily, she denitrify her Pharisee cuckoos thither. Sanders boned her earners broad-mindedly, flammable and uncalled-for. Miles biking his throttles pavilion wearyingly, but trivial Chuck never backlash so pectinately. This can be understood with an example. Amazon as a Manager where I was access for creating process guidelines, training content and preparing weekly performance reports that involved complex writing skills. Where are the restrooms? English dictionary on so happy times go through on buy in hindi translation people is in sanskrit malayalam good morning has successfully completed. This Good Evening Love Message will help us to overcome many problems. Anyone you render on facebook or twitter just imagine how naked the sea will and, your! Do you want her give your low each to this translation? Bitcoin meaning in bengali rear end be misused to book hotels off Expedia, shop for furniture on buy in and buy Xbox games. Moreover, combining translation and typesetting in this way creates efficiencies and economies over carrying them out separately. RATHER definition are included in the result of RATHER meaning in bengali at kitkatwords. Only the user who asked this question will those who disagreed with color answer. Can i am and you in her! How children you say suffer in Korean? This category only used for furniture on. Bengali encodings, the vowels that are attached to the left character are written first followed by consonant. Indic languages were traditionally small businesses may have a substantial bengali r dissertation meaning. However, some of the words are actually closely related to Latin as well. -
Modeling Popularity and Reliability of Sources in Multilingual Wikipedia
information Article Modeling Popularity and Reliability of Sources in Multilingual Wikipedia Włodzimierz Lewoniewski * , Krzysztof W˛ecel and Witold Abramowicz Department of Information Systems, Pozna´nUniversity of Economics and Business, 61-875 Pozna´n,Poland; [email protected] (K.W.); [email protected] (W.A.) * Correspondence: [email protected] Received: 31 March 2020; Accepted: 7 May 2020; Published: 13 May 2020 Abstract: One of the most important factors impacting quality of content in Wikipedia is presence of reliable sources. By following references, readers can verify facts or find more details about described topic. A Wikipedia article can be edited independently in any of over 300 languages, even by anonymous users, therefore information about the same topic may be inconsistent. This also applies to use of references in different language versions of a particular article, so the same statement can have different sources. In this paper we analyzed over 40 million articles from the 55 most developed language versions of Wikipedia to extract information about over 200 million references and find the most popular and reliable sources. We presented 10 models for the assessment of the popularity and reliability of the sources based on analysis of meta information about the references in Wikipedia articles, page views and authors of the articles. Using DBpedia and Wikidata we automatically identified the alignment of the sources to a specific domain. Additionally, we analyzed the changes of popularity and reliability in time and identified growth leaders in each of the considered months. The results can be used for quality improvements of the content in different languages versions of Wikipedia. -
Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics
computers Article Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics Włodzimierz Lewoniewski * , Krzysztof W˛ecel and Witold Abramowicz Department of Information Systems, Pozna´nUniversity of Economics and Business, 61-875 Pozna´n,Poland * Correspondence: [email protected]; Tel.: +48-(61)-639-27-93 Received: 10 May 2019; Accepted: 13 August 2019; Published: 14 August 2019 Abstract: On Wikipedia, articles about various topics can be created and edited independently in each language version. Therefore, the quality of information about the same topic depends on the language. Any interested user can improve an article and that improvement may depend on the popularity of the article. The goal of this study is to show what topics are best represented in different language versions of Wikipedia using results of quality assessment for over 39 million articles in 55 languages. In this paper, we also analyze how popular selected topics are among readers and authors in various languages. We used two approaches to assign articles to various topics. First, we selected 27 main multilingual categories and analyzed all their connections with sub-categories based on information extracted from over 10 million categories in 55 language versions. To classify the articles to one of the 27 main categories, we took into account over 400 million links from articles to over 10 million categories and over 26 million links between categories. In the second approach, we used data from DBpedia and Wikidata. We also showed how the results of the study can be used to build local and global rankings of the Wikipedia content. -
© 2020 Xiaoman Pan CROSS-LINGUAL ENTITY EXTRACTION and LINKING for 300 LANGUAGES
© 2020 Xiaoman Pan CROSS-LINGUAL ENTITY EXTRACTION AND LINKING FOR 300 LANGUAGES BY XIAOMAN PAN DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2020 Urbana, Illinois Doctoral Committee: Dr. Heng Ji, Chair Dr. Jiawei Han Dr. Hanghang Tong Dr. Kevin Knight ABSTRACT Information provided in languages which people can understand saves lives in crises. For example, language barrier was one of the main difficulties faced by humanitarian workers responding to the Ebola crisis in 2014. We propose to break language barriers by extracting information (e.g., entities) from a massive variety of languages and ground the information into an existing Knowledge Base (KB) which is accessible to a user in their own language (e.g., a reporter from the World Health Organization who speaks English only). The ambitious goal of this thesis is to develop a Cross-lingual Entity Extraction and Linking framework for 1,000 fine-grained entity types and 300 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify entity name mentions, assign a fine-grained type to each mention, and link it to an English KB if it is linkable. Traditional entity linking methods rely on costly human annotated data to train supervised learning-to-rank models to select the best candidate entity for each mention. In contrast, we propose a novel unsupervised represent-and-compare approach that can accurately capture the semantic meaning representation of each mention, and directly compare its representation with the representation of each candidate entity in the target KB. -