Adaptive Transliteration Based on Cross-Script Trie Generation: a Case Of
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Roman to Urdu Transliteration Using Word List
Roman to Urdu Transliteration using word list Tafseer Ahmed Universitaet Konstanz [email protected] Abstract script is widely used when there comes a need of a language for the informal communication over internet. The paper discusses transliteration of Urdu words Unlike Urdu script, the roman script for Urdu does from roman script to Urdu script. We propose a word not have any standard for spelling the words. A word list based approach that gives a better transliteration can be written in various forms not only by distinct to the Persio-Arabic letters that have same or similar writers but also by the same writer at different sound but are written as different letter in Urdu script. occasions. Specially, there is no one to one mapping We give a rule based system for roman to Urdu between Urdu letters for vowel sounds and the transliteration. As the roman script for Urdu does not corresponding roman letters. The support of Urdu follow any standard and a single word can be written script on the computers is getting better by the usage of in multiple ways, the proposed rule based system Unicode character set and Open Type fonts. The covers the different ways of writing and give the best availability of Urdu script support demands for roman possible Urdu transliteration based on word list and to Urdu transliterator that can convert the text written roman to Urdu script mapping rules. in roman Urdu into proper Urdu script. This paper focuses on the issues related to the 1. Introduction transliteration of Urdu text from roman script to Urdu script. -
And Others TITLE on the Uses of the Computer for Content Analysis in Educational Research
DOCUMENT RESUME ED 084 281 TM 003 289 AUTHOR Hiller, Jack H.; And Others TITLE On the Uses of the Computer for Content Analysis in Educational Research. PUB DATE Feb 73 NOTE 21p.; Revised version of paper presented at national conference of Association for Computing Machinery (San Francisco, August 1969) EDRS PRICE MF-$0.65 HC-$3.29 DESCRIPTORS Audiovisual Aids; Classification; *Computer Programs; *Content Analysis; Educational Research; *Measurement Instruments; *Scoring; Social Sciences; *Structural Analysis; Technical Reports ABSTRACT Current efforts to take advantage of the special virtues of the computer as an aid in text analysis are described. Verbal constructs, category construction, and contingency analysis are discussed and illustrated. Mechanical techniques for reducing human labor when studying large quantities of verbal data have been sought at an increasing rate by researchers in the behavioral sciences. Whatever the purpose of research, if it is to have a scientific character, ti must involve an attempt to reduce natural language data, by formal rules, to measures reflecting theoretically relevant properties of the text, its source, or its audience effects. At the present time, there is no one theory or method dominating the field of natural language analysis. Although much work is currently being expended to implement a finite set of rules on the computer, little has been accomplished that is directly useful to researchers in the social sciences. (Author/CK) A. e r-4 csJ U S DEPARTMENT OF HEALTH, EDUCATION & WELFARE NATIONAL INSTITUTE OF EDUCATION CO THIS DOCUMENT HAS BEEN REPRO oucccrExAcrLY AS RECEIVED FROM THE PERSON OR ORGANIZATION ORIGIN O ti NG IT POINTS OF VIEW OR OPINIONS STATED 00 NOT NECESSARILY REPRE SENT OFFICIAL NATIONAL INSTITUTE OF EDUCATION POSITION OR POLICY LiJ On the Uses of theComputer Research' ForContent Analysis in Educational Jack H. -
Urdu Alphabet 1 Urdu Alphabet
Urdu alphabet 1 Urdu alphabet Urdu alphabet ﺍﺭﺩﻭ ﺗﮩﺠﯽ Example of writing in the Urdu alphabet: Urdu Type Abjad Languages Urdu, Balti, Burushaski, others Parent systems Proto-Sinaitic • Phoenician • Aramaic • Nabataean • Arabic • Perso-Arabic • Urdu alphabet ﺍﺭﺩﻭ ﺗﮩﺠﯽ [1] Unicode range U+0600 to U+06FF [2] U+0750 to U+077F [3] U+FB50 to U+FDFF [4] U+FE70 to U+FEFF Urdu alphabet ﮮ ﯼ ء ﮪ ﻩ ﻭ ﻥ ﻡ ﻝ ﮒ ﮎ ﻕ ﻑ ﻍ ﻉ ﻅ ﻁ ﺽ ﺹ ﺵ ﺱ ﮊ ﺯ ﮌ ﺭ ﺫ ﮈ ﺩ ﺥ ﺡ ﭺ ﺝ ﺙ ﭦ ﺕ ﭖ ﺏ ﺍ Extended Perso-Arabic script • History • Diacritics • Hamza • Numerals • Numeration The Urdu alphabet is the right-to-left alphabet used for the Urdu language. It is a modification of the Persian alphabet, which is itself a derivative of the Arabic alphabet. With 38 letters and no distinct letter cases, the Urdu alphabet is typically written in the calligraphic Nasta'liq script, whereas Arabic is more commonly in the Naskh style. Usually, bare transliterations of Urdu into Roman letters (called Roman Urdu) omit many phonemic elements that have no equivalent in English or other languages commonly written in the Latin script. The National Language Authority of Pakistan has developed a number of systems with specific notations to signify non-English sounds, but ﺥ ﻍ ﻁ these can only be properly read by someone already familiar with Urdu, Persian, or Arabic for letters such as Urdu alphabet 2 [citation needed].ﮌ and Hindi for letters such as ﻕ or ﺹ ﺡ ﻉ ﻅ ﺽ History The Urdu language emerged as a distinct register of Hindustani well before the Partition of India, and it is distinguished most by its extensive Persian influences (Persian having been the official language of the Mughal government and the most prominent lingua franca of the Indian subcontinent for several centuries prior to the solidification of British colonial rule during the 19th century). -
Learning Trilingual Dictionaries for Urdu – Roman Urdu – English
Learning Trilingual Dictionaries for Urdu – Roman Urdu – English Moiz Rauf and Sebastian Padó Institut für Maschinelle Sprachverarbeitung University of Stuttgart, Germany {moiz.rauf,pado}@ims.uni-stuttgart.de Abstract In this paper, we present an effort to generate a joint Urdu, Roman Urdu and English trilingual lexicon using automated methods. We make a case for using statistical machine translation approaches and parallel corpora for dictionary creation. To this purpose, we use word alignment tools on the corpus and evaluate translations using human evaluators. Despite different writing script and considerable noise in the corpus our results show promise with over 85% accuracy of Roman Urdu–Urdu and 45% English–Urdu pairs. 1 Introduction Bilingual lexicons serve an integral role in cross lingual information retrieval and bringing NLP to low resourced languages. The process of dictionary generation has greatly benefited from improvements in statistical translation methods. However, for low resourced languages the large parallel and monolingual corpora necessary to learn these dictionaries are hard to come by and remain a critical hurdle (Lam et al., 2015). In this paper, we have developed such a resource for Urdu, English and Roman Urdu (Urdu written in Latin script) language pairs. Urdu is an Indo-Aryan language with an extended Persio-Arabic script. It is the national language of Pakistan (Rasul, 2013), while English has been established as the medium used in educational and official settings in the country (Rafi, 2013; Muhammad Asghar and Mahmood, 2013). Roman Urdu despite not being an official script, plays an important role in communication and is widely popular on social media platforms (Bilal et al., 2017). -
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Christos Kakalis In the Orality/Aurality of the Book Inclusivity and Liturgical Language Abstract This article examines the role of language in the constitution of a common identity through its liturgical use at the Eastern Orthodox church of St Andrew’s in Edinburgh, Scotland. Open to individuals who have relocated, the parish has a rather multination- al character. It is a place of worship for populations that consider Christian Orthodox culture part of their long-established collective identity and for recent converts. Based on ethnographic research, archival work and theoretical contextualisation, the article examines the atmospheric materiality of the written text as performed by the readers, the choir and the clergy. This soundscape is an amalgam of different kinds of reading: prose, chanted prose, chanting and antiphonic, depending the part of the Liturgy being read. The language of the book is performative: the choreography and its symbolisms perform the words of the texts and vice versa. Additionally, the use of at least four languages in every service and two Eastern Orthodox chanting styles in combination with European influences expresses in the most tangible way the religious inclusivity that has been carefully cultivated in this parish. Through closer examination of literary transformation processes, I demonstrate the role of liturgical language in the creation of communal space-times that negotiate ideas of home and belonging in a new land. Keywords Role of Language, Christian Orthodox Culture, Religious Books, Transnational Religious Community, Religious Soundscapes, Atmospheric Materiality, Performance, Belonging Biography Christos Kakalis holds a PhD in architecture from the Edinburgh School of Architecture and Landscape Architecture and is a Senior Lecturer in Architecture at the School of Ar- chitecture, Planning and Landscape of Newcastle University. -
109969 TESL Vol 25-1 Text
Inscribing Identity: Insights for Teaching From ESL Students’ Journals Jenny Miller Linguistic minority students in schools must acquire and operate in a second language while negotiating mainstream texts and content areas, along with negotiating an emerging new sense of social identity. This article presents jour- nal data from an Australian ethnographic study that explored the relationship between second-language use, textual practices in school, and the representation of identity. Such texts normally lie outside dominant school discourses, but for students they are a powerful means of negotiating identity and gaining vital language practice. For teachers journals provide critical insights into the experi- ences of their students and into their developing language competence. Les élèves qui forment une minorité linguistique à l’école doivent acquérir une langue seconde et fonctionner dans cette langue en même temps qu’ils assimilent de la matière académique dans la langue dominante et se construisent une nouvelle identité sociale. Cet article présente des données de journaux personnels provenant d’une étude ethnographique en Australie portant sur le rapport entre l’usage d’une langue seconde, les pratiques textuelles à l’école et la représentation de l’identité. Ce genre de textes ne fait habituellement pas partie du discours pédagogique dominant; pourtant, ces textes représentent pour les élèves un puissant moyen de négocier leur identité et de pratiquer leurs habiletés langa- gières. Les journaux personnels fournissent aux enseignants un aperçu impor- tant des expériences de leurs élèves et du développement de leurs compétences langagières. Introduction One of the most critical realities of contemporary education in a globalized world is the growing cultural, racial, and linguistic diversity in schools and the difficulties involved in educating and empowering vast numbers of students who do not speak the majority language. -
Sequence to Sequence Networks for Roman-Urdu to Urdu Transliteration
20th International Multitopic Conference (INMIC’ 17) Sequence to Sequence Networks for Roman-Urdu to Urdu Transliteration Mehreen Alam, Sibt ul Hussain [email protected], [email protected] Reveal Lab, Computer Science Department, NUCES Islamabad, Pakistan Abstract— Neural Machine Translation models have natural language processing methods are being employed for replaced the conventional phrase based statistical translation Urdu language which limit the extent to which the methods since the former takes a generic, scalable, data-driven performance can be achieved. Their reliance on hand-crafted approach rather than relying on manual, hand-crafted features. features and manual annotations restricts not only their The neural machine translation system is based on one neural scalability for larger datasets but also their capacity to handle network that is composed of two parts, one that is responsible varying complexities of the language. In this paper, neural for input language sentence and other part that handles the machine translation based on deep learning has been applied desired output language sentence. This model based on to get transliteration from Roman-Urdu to Urdu with distributed representations of both the languages. encoder-decoder architecture also takes as input the distributed representations of the source language which enriches the Neural Machine Translation is based on the concept of learnt dependencies and gives a warm start to the network. In encoder-decoder where encoder and decoder are both based on this work, we transform Roman-Urdu to Urdu transliteration the sequence of multiple Recurrent Neural Network (RNN) into sequence to sequence learning problem. To this end, we cells [3]. -
Consent Wikipedia in Hindi
Consent Wikipedia In Hindi Unhazardous Wolf repossess some towelings and overruled his fitting so inclusively! Alexis usually buckraming unreasonably or reasons flamboyantly when subnormal Hew pertains thrivingly and desperately. Berkeley is vestmented: she ameliorating fluently and perspire her titularity. Our clients to english, when ensuring you could be with wikipedia in hindi mera parivar Is refers to christ and the lucky number and religion is hurt in this balloon gender rating! Essay my teacher quality. There are true several meanings of each refugee in Urdu, the correct meaning of Checkroom in Urdu is توشە خانە, and in roman we amplify it Tosha Khanah. Urdu words especially used in combination with other words, karam meaning in urdu and quotations form. Find someone talking write my scratch paper. It helps you understand every word Bloodline with comprehensive detail, no other web page in our hair can explain Bloodline better than writing page. Our specialists hear in wikipedia reference, consent wikipedia in hindi with! Essay on learn that failure examples of appendix in essay. Another possible point which ran be noted is best if some information is denied to Legislature, this exemption does someone say it should however be given forth a citizen. Essay about batch and wellbeing. The ruling planet for an hindi school, wikipedia in hindi tv actress and? Quebec law a photographer can take photographs in public places but may be publish check picture unless permission has been obtained from one subject. Your consent is held that you can be there was just this book, hindi originated from all set in doing so that such consent wikipedia in hindi: argumentative essay assignment by volunteers around the. -
United States Patent (19) 11 Patent Number: 5,930,754 Karaali Et Al
USOO593.0754A United States Patent (19) 11 Patent Number: 5,930,754 Karaali et al. (45) Date of Patent: Jul. 27, 1999 54 METHOD, DEVICE AND ARTICLE OF OTHER PUBLICATIONS MANUFACTURE FOR NEURAL-NETWORK BASED ORTHOGRAPHY-PHONETICS “The Structure and Format of the DARPATIMIT CD-ROM TRANSFORMATION Prototype”, John S. Garofolo, National Institute of Stan dards and Technology. 75 Inventors: Orhan Karaali, Rolling Meadows; “Parallel Networks that Learn to Pronounce English Text' Corey Andrew Miller, Chicago, both Terrence J. Sejnowski and Charles R. Rosenberg, Complex of I11. Systems 1, 1987, pp. 145-168. 73 Assignee: Motorola, Inc., Schaumburg, Ill. Primary Examiner David R. Hudspeth ASSistant Examiner-Daniel Abebe 21 Appl. No.: 08/874,900 Attorney, Agent, or Firm-Darleen J. Stockley 22 Filed: Jun. 13, 1997 57 ABSTRACT A method (2000), device (2200) and article of manufacture (51) Int. Cl. .................................................. G10L 5/06 (2300) provide, in response to orthographic information, 52 U.S. Cl. ............... 704/259; 704/258; 704/232 efficient generation of a phonetic representation. The method 58 Field of Search ..................................... 704/259,258, provides for, in response to orthographic information, effi 704/232 cient generation of a phonetic representation, using the Steps of inputting an Orthography of a word and a predetermined 56) References Cited Set of input letter features, utilizing a neural network that has U.S. PATENT DOCUMENTS been trained using automatic letter phone alignment and predetermined letter features to provide a neural network 4,829,580 5/1989 Church. 5,040.218 8/1991 Vitale et al.. hypothesis of a word pronunciation. 5,668,926 9/1997 Karaali et al. -
An Unsupervised Method for Discovering Lexical Variations in Roman Urdu Informal Text
An Unsupervised Method for Discovering Lexical Variations in Roman Urdu Informal Text Abdul Rafae, Abdul Qayyum, Muhammad Moeenuddin, ‡ ‡ Asim Karim, Hassan Sajjad and Faisal Kamiran, Qatar Computing Research† Institute, Hamad‡ Bin Khalifa University Lahore† University of Management Sciences, Information Technology University ‡ Abstract tasks such as Urdu word segmentation (Durrani and Hussain, 2010), part of speech tagging (Saj- We present an unsupervised method to jad and Schmid, 2009), spell checking (Naseem find lexical variations in Roman Urdu and Hussain, 2007), machine translation (Durrani informal text. Our method includes a et al., 2010), etc. phonetic algorithm UrduPhone, a feature- In this paper, we propose an unsupervised based similarity function, and a clustering feature-based method that tackles above men- algorithm Lex-C. UrduPhone encodes ro- tioned challenges in discovering lexical variations man Urdu strings to their phonetic equiv- in Roman Urdu. We exploit phonetic and string alent representations. This produces an similarity based features and incorporate contex- initial grouping of different spelling vari- tual features via top-k previous and next words’ ations of a word. The similarity function features. For phonetic information, we develop an incorporates word features and their con- encoding scheme for Roman Urdu, UrduPhone, text. Lex-C is a variant of k-medoids clus- motivated from Soundex. Compared to other tering algorithm that group lexical varia- available phonetic-based schemes that are mostly tions. It incorporates a similarity thresh- limited to English sounds only, UrduPhone maps old to balance the number of clusters and Roman Urdu homophones effectively. Unlike pre- their maximum similarity. We test our sys- vious work on short text normalization (see Sec- tem on two datasets of SMS and blogs and tion 2), we do not have information about stan- show an f-measure gain of up to 12% from dard word forms in the dataset. -
Download Tetra En.Pdf
TextTransformer 1.7.5 © 2002-10 Dr. Detlef Meyer-Eltz 2 TextTransformer Table of Contents Part I About this help 16 Part II Registration 18 Part III Most essential operation elements 21 Part IV Most essential syntax elements 23 Part V How to begin with a new project? 25 1 Practice................................................................................................................................... 27 Part VI Introduction 30 1 How................................................................................................................................... does the TextTransformer work? 30 2 Analysis................................................................................................................................... 30 3 Synthesis................................................................................................................................... 31 4 Regular................................................................................................................................... expressions 31 5 Syntax................................................................................................................................... tree 32 6 Productions................................................................................................................................... or non-terminal symbols 33 7 Productions................................................................................................................................... as functions 33 8 Four.................................................................................................................................. -
Optical Character Recognition
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2 Issue-1, March 2013 Optical Character Recognition Ravina Mithe, Supriya Indalkar, Nilam Divekar Most of the character recognition program will be Abstract— The Optical Character Recognition is a mobile recognized through the input image with a scanner or a application. It uses smart mobile phones of android platform. digital camera and computer software. There is a problem in This paper combines the functionality of Optical Character the spatial size of the computer and scanner. If you do not Recognition and speech synthesizer. The objective is to develop have a scanner and a digital camera, a hardware problem user friendly application which performs image to speech occurs. In order to overcome the limitations of computer conversion system using android phones. The OCR takes image occupying a large space, character recognition system based as the input, gets text from that image and then converts it into speech. This system can be useful in various applications like on android phone is proposed [4]. banking, legal industry, other industries, and home and office OCR is a technology that enables you to convert different automation. It mainly designed for people who are unable to types of documents such as scanned paper documents, PDF read any type of text documents. In this paper, the character files or images captured by a digital camera into editable and recognition method is presented by using OCR technology and searchable data. Images captured by a digital camera differ android phone with higher quality camera. from scanned documents or image.