Top PDF English to Urdu and Roman Urdu Dictionary

English to Urdu and Roman Urdu Dictionary

English to Urdu and Roman Urdu Dictionary

یفاعم ماعâaam mo^aafi / رزگ ردdar guzar یگناوید نلتاق ہ qaatilaanah diiwaañgi. ad[r]

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Reading across Different Orthographies: Urdu, Arabic, Hindi and English

Reading across Different Orthographies: Urdu, Arabic, Hindi and English

on their own instead they are applied above or below a consonant by using appropriate diacritics and the primary orthographic structure of Urdu is similar to Arabic (Humayoun, & Hammarstrom, 2006). On the other hand, English (L2 of this bilingual group) is considered a deep orthographic language with more complex grapheme-phoneme correspondence and more irregularities in its writing system. Urdu-English bilinguals from Pakistan and Canada differed from each other in terms of the processes they used in learning to read and speak English as their L2. For example, Urdu-English bilinguals from Pakistan performed noticeably higher on Urdu variables as compared to English variables tested in the study. On the other hand, Urdu-English bilinguals from Canada performed better on English variables as compared to Urdu variables tested in the study. Urdu-English bilinguals from Pakistan learn to read English prior to learning to speak at schools at the age of eight or nine (Grade 4 to 5). Whereas, Urdu-English bilinguals in Canada learn to speak Urdu and English simultaneously in their homes and learn to read English prior to learning to read Urdu in their schools at the age of five. In addition to learning spoken Urdu, Muslim children from Pakistan learn to read Arabic script. As the language of the Quran, Arabic is also widely used throughout the Muslim world and attached to the Muslim community. Therefore, it was expected that bilingual children who get more exposure to their L1 (Urdu) and Arabic as another language with similar scripts would achieve a higher level of oral language and reading skills as compared to the bilinguals who only learned L1 and L2 with limited exposure in foreign context (Arabic-English bilinguals in Canada). Based on the previous findings of Seymour and colleagues (2003) who demonstrated that readers of shallow and
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An order effect in English infants’ discrimination of an Urdu affricate contrast

An order effect in English infants’ discrimination of an Urdu affricate contrast

In Experiment 2, the subgroup habituated to the aspirated affricate behaved similarly to the 7-month-olds, showing no signs of developmental decline, unlike the other subgroup, which was habituated to the native-like aspirated affricate. The results make the evidence for perceptual narrowing more complex, since the older group of infants showed signs of developmental decline when presented with the stimuli in one order but not in the other, with the very same contrast. This suggests that the perceptual narrowing observed in infants at the end of first year may depend upon additional factors beyond those considered so far. At the end of the first year infants tend to show a decline in the perception of non-native contrasts that are not functional in the ambient language. However, if at that age infants can show discrimination for the non-native contrast, without special training, when presented with the stimuli in a specific order, what does that mean for the finding of perceptual narrowing? Is perceptual narrowing merely a function of task characteristics? Or do the order effects found here tell us someth ing about infants’ sound representations? We will come back to this in the main discussion. But first we test whether adult English speakers show insensitivity to the contrast /t / - /t / and whether this ‘narrowing’ of their perception is advanced to such an extent that they cannot discriminate between the two sounds, even when presented with the aspirated affricate first. In order to investigate that issue we compared the performance of adult English speakers to a group of native Urdu-speaking adults. 11.0 Experiment 3: Is the asymmetry in discrimination for non-native consonants maintained in adulthood?
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Rule Based English to Urdu Machine Translation

Rule Based English to Urdu Machine Translation

3. Tense Aspect Modality (TAM) English uses auxiliaries to mark the TAM of a sentence, while Urdu verbs take post position for describing TAM; there is no one-to-one mapping between auxiliaries of English and Urdu post position. Moreover, verbs in Urdu also inflect according to the GNP of the noun phrase. Tense of a sentence is determined by the sequence and form of auxiliary verbs, before the main verb and form of the main verb. TAM table is then used to identify tense of the sentence with the help of auxiliary verb and main verb form and determine Urdu verb suffix corresponding to it. Subject NP’s attribute (gender, name and person) is required for inflection of verb. We don’t deal with morphology and auxiliary verb separately; may be it is unconventional, but it speeds up the processing and increases efficiency with seemingly no disadvantage.
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An order effect in English infants’ discrimination of an Urdu affricate contrast

An order effect in English infants’ discrimination of an Urdu affricate contrast

In Experiment 2, the subgroup habituated to the aspirated affricate behaved similarly to the 7-month-olds, showing no signs of developmental decline, unlike the other subgroup, which was habituated to the native-like aspirated affricate. The results make the evidence for perceptual narrowing more complex, since the older group of infants showed signs of developmental decline when presented with the stimuli in one order but not in the other, with the very same contrast. This suggests that the perceptual narrowing observed in infants at the end of first year may depend upon additional factors beyond those considered so far. At the end of the first year infants tend to show a decline in the perception of non-native contrasts that are not functional in the ambient language. However, if at that age infants can show discrimination for the non-native contrast, without special training, when presented with the stimuli in a specific order, what does that mean for the finding of perceptual narrowing? Is perceptual narrowing merely a function of task characteristics? Or do the order effects found here tell us someth ing about infants’ sound representations? We will come back to this in the main discussion. But first we test whether adult English speakers show insensitivity to the contrast /t / - /t / and whether this ‘narrowing’ of their perception is advanced to such an extent that they cannot discriminate between the two sounds, even when presented with the aspirated affricate first. In order to investigate that issue we compared the performance of adult English speakers to a group of native Urdu-speaking adults. 11.0 Experiment 3: Is the asymmetry in discrimination for non-native consonants maintained in adulthood?
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Urdu to English Machine Translation using Bilingual Evaluation Understudy

Urdu to English Machine Translation using Bilingual Evaluation Understudy

Paninian grammar. Another common technique for MT is Statistical Machine Translation (SMT). It uses probabilities for translating text from one language to another. Bushra et al used SMT technique to investigate issues in machine translation between languages with significant word order differences [6]. The third common MT technique is the Example Based Machine Translation (EBMT) that translates input text by matching examples from large amount of training data. Maryam and Zafar presented Example based approach that translates text form English to Urdu. It supports idioms, homographs, and some other linguistic features [7]. Parallel corpus for statistical machine translation for English text into Urdu was presented by Aasim et al [8]. “Word Order Issues in English to Urdu Machine Translation” was presented by B. Jawaid and D. Zeman [9]. M. Zhang and H. Li investigated issues related to phrase reordering [20]. In addition to the aforementioned techniques, SMT systems such as Google and Bing are available online [10-11]. However they provide limited accuracy due to the intrinsic issues in Urdu Machine Translation.
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URDU ENGLISH CODE SWITCHING AND CODE MIXING IN INDIA  A CASE STUDY OF THE USE OF URDU HINDI AT PHRASES AND CLAUSES LEVELS IN INDIAN HINGLISH (HINDI ENGLISH)

URDU ENGLISH CODE SWITCHING AND CODE MIXING IN INDIA A CASE STUDY OF THE USE OF URDU HINDI AT PHRASES AND CLAUSES LEVELS IN INDIAN HINGLISH (HINDI ENGLISH)

Hinglish has assumed a linguistic and cultural identity of its own. This identity manifests itself throughout the language at the word level, the phrase level and the sentence level. It is the natural consequence of its regular contact with the Urdu/Hindi language. A large number of borrowings from Urdu/Hindi and the regional languages have entered in Hinglish (Baumgardner 1993). Certain lexical items may show a shift from their original Standard British English usage to Urduized meaning (Talaat 1993). In comparison with the borrowing in syntax and morphology, lexical items have the highest ease of borrowing and seem most likely to occur (Brutt-Griffler, 2002; Romaine, 1995). Such a vocabulary items in all the new varieties of English are largely drawn from the areas that are significantly different to the geo-social and cultural context of British English (Fernado 2003). The code-switching data in this paper focuses on the use of Hindi/Urdu phrases and clauses in the English language and shows that its occurrence imposes no ungrammatical effect on the structure of English syntax. The data has been collected from the following printed Indian English newspaper and magazines:
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A Linguistic Study of Borrowings from English to Urdu

A Linguistic Study of Borrowings from English to Urdu

In countries where English is a second language, the range and depth of its function is more than a foreign language as English becomes integral to the workings of a country (D’Souza, 2001). After independence of Pakistan the influence of Arabic and Persian was almost finished as the government retained English as the second language of the country. Government introduced bilingualism in educational field. It was the time when Urdu began accepting the influence of a language which was fully equipped, modern and was also the language of their rulers in the past. Government policies have always been English-friendly in Pakistan. There have been two streams of education in terms of language since independence i.e. Urdu and English. Both are used side by side. Even now, two medium are existing in almost every field of life. So, English words entered Urdu without any obstacle. With the passage of time, frequency of borrowing words increased so much that Urdu seems to lose its ground in Pakistan. Countless English words and expressions have entered Urdu and most of them even don’t have their equivalents in Urdu.
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A Cross-Cultural Study of Apologies in British English and Urdu

A Cross-Cultural Study of Apologies in British English and Urdu

The current study however goes in line with the findings of many other researchers (Aijmer 1996; Blum- Kulka and Olshtain 1984; Deutschmann 2003; Holmes 1990; Bean and Johnstone 1994; Meier 1992; and Owen 1983) in describing sorry as the most favoured formulaic expression of apology. It is also interesting to state that English expression sorry has also appeared frequently in Urdu data. Sorry and its Urdu equivalent called ‘afsoos hona’ have appeared as the most frequent IFID expressions (67%) among speakers of this language. Pardon (25%), in English and its Urdu equivalent ‘Mahzret chhana’ (18.6%) are second most frequently applied IFIDs in both the languages. Like sorry, another IFID called excuse is also directly used in Urdu apologies. Researcher, being a member of Urdu speaking population, personally knows that excuse is usually used to draw attention of someone, or to apologize in case of minor offences. Direct and frequent application of these two IFIDs can be termed as colonization of Urdu language due to rapid spread of English in the country. Forgive, is the least used IFID formula in English (0.8%). Similarly, its Urdu equivalent called Mahfi chahana/ Mahf kerna has made no
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Urdu-English Cross-Language Question Answering System

Urdu-English Cross-Language Question Answering System

From the comparison table, it is clear that the loss of accuracy is not much significant. However, efficiency of the system is reduced by putting the Translator in front. This is because when the system had translated the question from Urdu to English, the original phrase losses the standard question structure. Thus the system, unaware of these uncertain changes, sometimes reformulates the question phrase, losing the meaning. Moreover the connecting words in a question, like is, was, did, does, etc, are usually omitted during the translation procedures, thereby producing a improper sentence phrase in verb reformulations.
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English to Urdu Statistical Machine Translation: Establishing a Baseline

English to Urdu Statistical Machine Translation: Establishing a Baseline

Our parallel corpus consists of around 79K sentences collected from five different sources. The collection comes from several domains such as News, Religion, Technology, Language and Culture etc. 95% of the data is used for training, whereas the rest is evenly split into dev and test sets. • Emille: EMILLE (Enabling Minority Language Engineering) (Baker et al., 2002) is a col- lection of monolingual (written and spoken), parallel and annotated corpora of fourteen South Asian languages which is distributed by the European Language Resources Associa- tion (ELRA). The Urdu-English part are documents produced by the British Departments of Health, Social Services, Education and Skills, and Transport, Local Government and the Regions of British government translated into Urdu.
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Urdu and English in E-discourse Variation in the  Theme of Linguistic Hegemony

Urdu and English in E-discourse Variation in the Theme of Linguistic Hegemony

Lexicons are among the components of the Urdu language which are more easily and radically affected. As many as 974 English basic words were found in mixed Urdu and English posts. Nevertheless, I have tabulated most frequently occurring non-basic English words in table 1. The exact number of non-basic English words in Urdu may have exceeded far more than what we calculated if we had expanded the canvas of our investigation over to no-equivalent forms. It is hard to predict how many of these words will further establish in the Urdu language but we may speculate that the words which are more frequent in Urdu conversation will accumulate a strong tendency to replace their counterparts. A fair number of these words appeared in Urdu with the advent of electronic communication. Many of these words may have found their way into Urdu because of prestigious connotations or the fact that their counterparts sound too cumbersome to use in mainstream language or the participants might not know their equivalent forms. Non-basic English words were often saturated into the weak and unmarked class of Urdu language.
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Urdu Vowel System and Perception of English Vowels by Punjabi-Urdu Speakers

Urdu Vowel System and Perception of English Vowels by Punjabi-Urdu Speakers

15 1.5 Aims and Objectives The present study investigates the acoustic and phonetic properties of Urdu vowels (monophthongs and diphthongs) and the perceptual assimilation of SSBE vowels by Punjabi-Urdu speakers who learn English at school. According to L2 speech learning theories (SLM, PAM, PAM-L2 and L2LP), L2 learners face difficulties in the perception and production of L2 segments (i.e. vowels and consonants) that are either new (do not occur in their L1) or are very close (phonetically) to L1 segments. In contrast to previous studies (e.g. Strange et al., 2007; Gilichinskaya and Strange, 2010), the listeners were not naïve learners of SSBE; however, it must be noted that they learn English in Pakistan and as a result the input (especially speaking and listening) they receive has very little to do with Received Pronunciation (RP) or SSBE, except for some audio-visual materials used in classrooms, and TV, films and other media platforms in everyday life. The present study investigates if the predictions of PAM, L2LP and SLM are applicable to the perception of SSBE vowels when L2 (English) is learned from a very young age in a non- native context and used as lingua franca in everyday life; hence the L2 users are not inexperienced learners.
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Content Analysis of Language Textbooks (English, Urdu, Sindhi) for Inclusivity

Content Analysis of Language Textbooks (English, Urdu, Sindhi) for Inclusivity

Cheng and Beigi (2011) quoted a Chinese saying: “children who learn together, learn to live together” (p. 240) and this is true in any classroom. English has gained a sta- tus that cannot be given to any other regional languages spoken in Pakistan, including the national language of Pakistan, Urdu, claimed (Zaidi & Zaki, 2017). They also argued that governments, provincial and federal, are aware of students’ struggle to learn English but they will continue enforcing their policy of teaching English to students from early classes. This has presented an opportunity to Textbook writers in Pakistan to present suc- cess stories of IPID from Pakistan and from other countries to make IWoPID aware of the achievement of IPID. These stories and discussion about these storied in the classroom will make students know more about IPID and their achievements locally and interna- tionally and shed their biases tied to their traditional concept of what IPID can and cannot do.
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(Books in English, Punjabi, Hindi & Urdu)

(Books in English, Punjabi, Hindi & Urdu)

In addition to several manuscripts relating to Guru Gobind Singh, a large number of books in English, Punjabi, Hindi and Urdu have been written. An attempt has been made to cover all the books however, entries of some books may not be in the list for want of availability. Articles appeared in the newspapers have not been covered for lack of references and footnotes. This bibliography covers books in English, Punjabi, Hindi and Urdu; selected articles in English & Punjabi; Ph.D. theses and manuscripts available in Bhai Gurdas Central Library, Guru Nanak Dev University, Amritsar. In preparing this bibliography the major role has been played by Mrs. Kulvir Kaur Assistant Librarian in Bhai Gurdas Central Library. I am thankful to all those library professionals who have made some contribution in this project.
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Enriching Source for English to Urdu Machine Translation

Enriching Source for English to Urdu Machine Translation

This paper focuses on the generation of case markers for free word order languages that use case markers as phrasal clitics for marking the relationship between the dependent- noun and its head. The generation of such clitics becomes essential task especially when translating from fixed word order languages where syntactic relations are identified by the positions of the dependent-nouns. To address the problem of missing markers on source-side, artificial markers are added in source to improve alignments with its target counterparts. Up to 1 BLEU point increase is observed over the baseline on different test sets for English-to-Urdu.
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English to Urdu Hierarchical Phrase based Statistical Machine Translation

English to Urdu Hierarchical Phrase based Statistical Machine Translation

The 4th Workshop on South and Southeast Asian NLP WSSANLP, International Joint Conference on Natural Language Processing, pages 72–76, Nagoya, Japan, 14-18 October 2013.... ºï ðòïï ðòðç.[r]

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urdu english paragraph english translations, paragraph english urdu translation english urdu english

urdu english paragraph english translations, paragraph english urdu translation english urdu english

addresses the similarities and differences of a urdu topic. But having waited to write the opening and translation sections, you need to review and paragraph them several times to catch up. For example, one may explore inherent contradictions within the text. -

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Finding Topics in Urdu: A Study of Applicability of Document Clustering in Urdu Language

Finding Topics in Urdu: A Study of Applicability of Document Clustering in Urdu Language

Urdu is a highly spoken South Asian language which is written in Arabic script. It has a number of differentiating features as compared to English. Firstly, Urdu has a strong case marking system [15] [16]. It uses different clitics to mark the cases which are also referred as postpositions. Urdu also has prepositions but there are very few examples in the corpus. Secondly, Urdu is highly rich in morphology as compared to English [17] [18]. According to [18], an Urdu verb can have more than 50 surface forms. It also has different surface forms for causatives and double causatives. Thirdly, Urdu has a structure where nouns, adjectives or quantifiers give verbal sense when following a light verb. This phenomenon is referred as complex predicate structure [19]. Urdu frequently uses auxiliary verbs which make its verbal structure way complex from English. To perform topic modeling on Urdu text, preprocessing is an important task due to its differences with English. For this purpose part of speech tagged documents have been used [20] [21] [22]. POS tagging is very helpful while performing preprocessing which is discussed in coming section.
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Experiences in Building Urdu WordNet

Experiences in Building Urdu WordNet

Hindi WordNet (HWN) is lexical database inspired by the English WordNet (Miller, 1993).The words in HWN are grouped together according to their similarity of meanings. Two words that can be interchanged in a context are synonymous in that context. Synsets or the synonym sets are the basic building blocks of HWN. For each word there is a synonym set, or synsets representing one lexical concept. There are 10 relations in HWN; Synonymy, Hypernymy / Hyponymy, Antonymy, Meronymy / Holonymy, Gradation, Entailment, Troponymy and Causative (Dipak , 2002). The Hindi WordNet deals only with the open class words. Thus, HWN contains the following categories of words. The details of Hindi WordNet are given in Table 1.
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