About: Latin script is a research topic. Over the lifetime, 320 publications have been published within this topic receiving 3201 citations. The topic is also known as: Roman script & Latn.
Papers published on a yearly basis
••16 Aug 1998
TL;DR: This work addresses the reverse problem: given a foreign name or loanword in Arabic text, the authors want to recover the original in Roman script, and presents results and examples of use in an Arabic-to-English machine translator.
Abstract: It is challenging to translate names and technical terms from English into Arabic. Translation is usually done phonetically: different alphabets and sound inventories force various compromises. For example, Peter Streams may come out as [Abstract contained text which could not be captured.] bytr strymz. This process is called transliteration. We address here the reverse problem: given a foreign name or loanword in Arabic text, we want to recover the original in Roman script. For example, an input like [Abstract contained text which could not be captured.] bytr strymz should yield an output like Peter Streams. Arabic presents special challenges due to unwritten vowels and phonetic-context effects. We present results and examples of use in an Arabic-to-English machine translator.
01 Jan 2007
TL;DR: The road to standardization of Roman Latin in the Third and Second Centuries BC can be traced back to the late Republic and Early Empire of the Roman Empire, see.
Abstract: Preface. 1 Latin and Indo-European. 2 The Languages of Italy. 3 The Background to Standardization. 4 'Old' Latin and its Varieties in the Period c.400--150 BC. 5 The Road to Standardization: Roman Latin of the Third and Second Centuries BC. 6 Elite Latin in the Late Republic and Early Empire. 7 Sub-Elite Latin in the Empire. 8 Latin in Late Antiquity and Beyond. Glossary. Appendix: The International Phonetic Alphabet. Bibliography of Reference and Other Works. Index.
16 Dec 2020
TL;DR: The HASOC track as mentioned in this paper is dedicated to evaluate technology for finding offensive language and hate speech, which has attracted much interest and over 40 research groups have participated as well as described their approaches in papers.
Abstract: This paper presents the HASOC track and its two parts. HASOC is dedicated to evaluate technology for finding Offensive Language and Hate Speech. HASOC is creating test collections for languages with few resources and English for comparison. The first track within HASOC has continued work from 2019 and provided a testbed of Twitter posts for Hindi, German and English. The second track within HASOC has created test resources for Tamil and Malayalam in native and Latin script. Posts were extracted mainly from Youtube and Twitter. Both tracks have attracted much interest and over 40 research groups have participated as well as described their approaches in papers. In this overview, we present the tasks, the data and the main results.
30 Mar 1990
TL;DR: The most comprehensive and up-to-date account in any language of the history of Latin script was given by the greatest living authority on medieval palaeography as discussed by the authors, who also provided a detailed account of the role of the book in cultural history from antiquity to the Renaissance.
Abstract: This work, by the greatest living authority on medieval palaeography, offers the most comprehensive and up-to-date account in any language of the history of Latin script. It also contains a detailed account of the role of the book in cultural history from antiquity to the Renaissance, which outlines the history of book illumination. Designed as a textbook, it contains a full and updated bibliography. Because the volume sets the development of Latin script in its cultural context, it also provides an unrivalled introduction to the nature of medieval Latin culture. It will be used extensively in the teaching of latin palaeography, and is unlikely to be superseded.
01 Jul 2018
TL;DR: A novel tweet dataset, titled Hindi- English Offensive Tweet (HEOT) dataset, consisting of tweets in Hindi-English code switched language split into three classes: non-offensive, abusive and hate-speech is introduced.
Abstract: The exponential rise of social media websites like Twitter, Facebook and Reddit in linguistically diverse geographical regions has led to hybridization of popular native languages with English in an effort to ease communication. The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script. The paper introduces a novel tweet dataset, titled Hindi-English Offensive Tweet (HEOT) dataset, consisting of tweets in Hindi-English code switched language split into three classes: non-offensive, abusive and hate-speech. Further, we approach the problem of classification of the tweets in HEOT dataset using transfer learning wherein the proposed model employing Convolutional Neural Networks is pre-trained on tweets in English followed by retraining on Hinglish tweets.