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Letteratura scientifica selezionata sul tema "Tamil lexicon"

Autore: Grafiati

Pubblicato: 29 luglio 2024

Ultima modifica: 31 luglio 2024

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Articoli di riviste sul tema "Tamil lexicon"

Smith, Ian. "Comments on Nordhoff ’s “Establishing and Dating Sinhala Influence in Sri Lanka Malay”". Journal of Language Contact 5, n. 1 (2012): 58–72. http://dx.doi.org/10.1163/187740912x623406.

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Students of Sri Lanka Malay agree that the language has been heavily influenced by the local languages, Sinhala and Tamil. Differences arise over not only the degree and timing of such influence from each language, but also the extent to which the language developed through untutored second language acquisition (on the part of Tamil &/or Sinhala speakers) &/or intense bilingualism (on the part of Malay speakers). Nordhoff’s arguments for Sinhala influence are examined in the context of Thomason’s (2001) framework for establishing contact-induced change and found to be convincing for some features, but weaker or unconvincing in others. The argument for early Sinhala phonological influence is based on an unsurprising distribution and the mechanism of substrate influence (Siegel, 1998, 2008) which has not been shown to operate in the context of intense bilingualism. The linguistic differing consequences of untutored second language acquisition and intense bilingualism have not been thoroughly investigated, except on lexicon (Thomason and Kaufman, 1988). The Sinhalese component of Sri Lanka Malay lexicon stands at less than 1% (Paauw, 2004), a figure inconsistent with the claim of heavy Sinhala influence through intense bilingualism.

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A, Gurumoorthy. "Women in Pulavar Kulanthai’s Ravana Kaviyam". International Research Journal of Tamil 3, n. 1 (28 gennaio 2021): 181–88. http://dx.doi.org/10.34256/irjt21120.

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‘Porul Thodarnilai Ceyyul’ was the name given to epic before the word kāppiyam came into existence. Tamil lexicon refers ‘kāppiyam’ as Sanskrit term. Kāvyam is the word used by Sanskrit scholars for ‘kāviyam’. Ravana Kāviyam written by Pulavar Kulanthai consists of 56 padalams (Chapters) of 2828 Viruttangal i.e., poems. He adopts the story of Ramayana as it is. He is a person who follows Periyar’s ideology of self-respect, feminism etc. His passion for Tamil makes him write many of his creative writings. Periyar advised women to learn all arts, particularly the art of self-defence. Kambar had depicted Sita as Rama’s wife in his epic. The relationship of Rama and Sita varies in various Ramayanas available in India. Ravana kāviyam doesn’t deviate from the parameters of epic. It stands within its grammar. Pulavar Kulanthai portraits women characters with dignity modesty of women.

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Irvine, Ann, e Chris Callison-Burch. "A Comprehensive Analysis of Bilingual Lexicon Induction". Computational Linguistics 43, n. 2 (giugno 2017): 273–310. http://dx.doi.org/10.1162/coli_a_00284.

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Bilingual lexicon induction is the task of inducing word translations from monolingual corpora in two languages. In this article we present the most comprehensive analysis of bilingual lexicon induction to date. We present experiments on a wide range of languages and data sizes. We examine translation into English from 25 foreign languages: Albanian, Azeri, Bengali, Bosnian, Bulgarian, Cebuano, Gujarati, Hindi, Hungarian, Indonesian, Latvian, Nepali, Romanian, Serbian, Slovak, Somali, Spanish, Swedish, Tamil, Telugu, Turkish, Ukrainian, Uzbek, Vietnamese, and Welsh. We analyze the behavior of bilingual lexicon induction on low-frequency words, rather than testing solely on high-frequency words, as previous research has done. Low-frequency words are more relevant to statistical machine translation, where systems typically lack translations of rare words that fall outside of their training data. We systematically explore a wide range of features and phenomena that affect the quality of the translations discovered by bilingual lexicon induction. We provide illustrative examples of the highest ranking translations for orthogonal signals of translation equivalence like contextual similarity and temporal similarity. We analyze the effects of frequency and burstiness, and the sizes of the seed bilingual dictionaries and the monolingual training corpora. Additionally, we introduce a novel discriminative approach to bilingual lexicon induction. Our discriminative model is capable of combining a wide variety of features that individually provide only weak indications of translation equivalence. When feature weights are discriminatively set, these signals produce dramatically higher translation quality than previous approaches that combined signals in an unsupervised fashion (e.g., using minimum reciprocal rank). We also directly compare our model's performance against a sophisticated generative approach, the matching canonical correlation analysis (MCCA) algorithm used by Haghighi et al. ( 2008 ). Our algorithm achieves an accuracy of 42% versus MCCA's 15%.

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Nordhoff, Sebastian. "Multi-verb constructions in Sri Lanka Malay". Journal of Pidgin and Creole Languages 27, n. 2 (13 agosto 2012): 303–43. http://dx.doi.org/10.1075/jpcl.27.2.04nor.

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This paper investigates serial verbs and related constructions in Sri Lanka Malay and shows that at least four types have to be distinguished (Motion Verb Serialization, Vector Verb Serialization, Compound Verbs, Clause Chains). The constructions found are quite different from those found in Atlantic or Pacific Creoles. This is due to the different input languages: Two of the constructions can be traced to influence from the local languages Tamil and/or Sinhala; one is of Indonesian origin, and one is mixed. Sri Lanka Malay is thus not a simple combination of South Asian Grammar and Malay lexicon but also shows retentions of Malay grammar, as already demonstrated by Slomanson (2006). This recombination of features can only be explained with an account which acknowledges the possibility of grammatical contributions from all input languages, whether substrate, superstrate, or any other.

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Kulkarni, Dhanashree S., e Sunil S. Rodd. "Sentiment Analysis in Hindi—A Survey on the State-of-the-art Techniques". ACM Transactions on Asian and Low-Resource Language Information Processing 21, n. 1 (31 gennaio 2022): 1–46. http://dx.doi.org/10.1145/3469722.

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Sentiment Analysis (SA) has been a core interest in the field of text mining research, dealing with computational processing of sentiments, views, and subjective nature of the text. Due to the availability of extensive web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. It has become extremely significant to analyze this data and recover valuable and relevant information. Hindi being the first language of the majority of the population in India, SA in Hindi has turned out to be a critical task particularly for companies and government organizations. This research portrays a systematic review specifically in the field of Hindi SA. The major contribution of this article includes the categorization of numerous articles based on techniques that have attracted researchers in performing SA tasks in Hindi language. This survey classifies these state-of-the-art computational intelligence techniques into four major categories namely lexicon-based techniques, machine learning techniques, deep learning techniques, and hybrid techniques. It discusses the importance of these techniques based on different aspects such as their impact on the issues of SA, levels of analysis, and performance evaluation measures. The research puts forward a comprehensive overview of the majority of the work done in Hindi SA. This study will help researchers in finding out resources such as annotated datasets, linguistic resources, and lexical resources. This survey delivers some significant findings and presents overall future research directions in the field of Hindi SA.

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Tien, Adrian. "Offensive language and sociocultural homogeneity in Singapore". International Journal of Language and Culture 2, n. 2 (7 dicembre 2015): 142–68. http://dx.doi.org/10.1075/ijolc.2.2.01tie.

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Offensive language use in Singapore’s languacultures appears to be underpinned by cultural norms and values embraced by most if not all Singaporeans. Interviews with local informants and perusal of Singapore’s linguistic and cultural resources led to the identification of eight offensive words and phrases deemed representative of Singaporean coarseness. This set was narrowed down to a smaller set of common words and phrases, all Chinese Hokkien, all culturally laden. The finding that, although originally Hokkien, all of them are accessible not only to the Chinese-speaking population but also to speakers of Singapore Malay, Singapore Tamil, and Singapore English is compelling. The words and phrases studied in this paper are full-fledged members of the lexicon of these local non-Chinese languages, without loss or distortion of meaning. They are accepted as part of the local linguistic scene and of local cultural knowledge. At least in certain situations, people of different ethnic backgrounds who live and work together can rely on them as a testament of common identity which, in a curious way, gives voice to the sociocultural homogeneity this society unrelentingly pursues.

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Gunna, Sanjana, Rohit Saluja e Cheerakkuzhi Veluthemana Jawahar. "Improving Scene Text Recognition for Indian Languages with Transfer Learning and Font Diversity". Journal of Imaging 8, n. 4 (23 marzo 2022): 86. http://dx.doi.org/10.3390/jimaging8040086.

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Reading Indian scene texts is complex due to the use of regional vocabulary, multiple fonts/scripts, and text size. This work investigates the significant differences in Indian and Latin Scene Text Recognition (STR) systems. Recent STR works rely on synthetic generators that involve diverse fonts to ensure robust reading solutions. We present utilizing additional non-Unicode fonts with generally employed Unicode fonts to cover font diversity in such synthesizers for Indian languages. We also perform experiments on transfer learning among six different Indian languages. Our transfer learning experiments on synthetic images with common backgrounds provide an exciting insight that Indian scripts can benefit from each other than from the extensive English datasets. Our evaluations for the real settings help us achieve significant improvements over previous methods on four Indian languages from standard datasets like IIIT-ILST, MLT-17, and the new dataset (we release) containing 440 scene images with 500 Gujarati and 2535 Tamil words. Further enriching the synthetic dataset with non-Unicode fonts and multiple augmentations helps us achieve a remarkable Word Recognition Rate gain of over 33% on the IIIT-ILST Hindi dataset. We also present the results of lexicon-based transcription approaches for all six languages.

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Gaur, Albertine. "Lexicon of Tamil Literature. By Kamil V. Zvelebil. (Handbuch der Orientalistik. Abteilung 2. Indien. Band 9.) pp. XXVII, 783. Leiden etc., E. J. Brill, 1994. NLG 450, US $257.25". Journal of the Royal Asiatic Society 6, n. 1 (aprile 1996): 132–33. http://dx.doi.org/10.1017/s1356186300015133.

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V, Ambika, e Sam Gideon S. "Lexical Theoretical Development in Applied Tamil Grammar Texts". International Research Journal of Tamil 4, S-18 (8 dicembre 2022): 7–19. http://dx.doi.org/10.34256/irjt224s182.

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After 19th century Tamil literature has gone through many dimensions. Advent of print media, focus on language education and the establishment of new educational institutions are the reason for the development of many language based grammatical texts. The newly added grammatical elements are recorded in the grammar texts. Applied Tamil grammar texts explains the syllable, series and words based on grammar books. However, the new modern language theory records the changes that have appeared in the language system and highlights the language and grammar in the theory of linguistics. Tamil grammar books explains and defines grammar based on a Tamil text Nannul. Types of words are explained in the linguistic point of view by adapting the etymology mentioned in the Nannul. It also explains the new adjectives and adverbs and records the newly developed grammatical elements which are used in modern language. Word classification is divided into three levels. They are, classification based on alphabetic, classification based on case of words and classification based on grammatical usage. Contemporary Tamil tradition examines some of the techniques adopted by Tamil grammarians to define the word. Present Tamil grammarians distinguishes nouns and verbs on the basis of verb or on the basis of object. People also began approaching Tamil Grammar based on English grammar because of the abundance usage of English language. Tamil grammar text ‘Nalla Nool Eluthavaenduma’ explains grammar in a very simple way and it is considered to be the best grammar manual. Tamil grammar text explores the structure of Tamil language. Lexical grammar is explained in terms of linguistics. The four types of words such as noun, verb, interjection and adjective examine the changes that occurred in the language. Thus, the article gives a clear idea about lexical theoretical development in applied Tamil grammar texts.

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Suresh Pande. "The Poetry of Syed Ameeruddin: A Thematic Appraisal". Creative Launcher 8, n. 5 (31 ottobre 2023): 103–12. http://dx.doi.org/10.53032/tcl.2023.8.5.11.

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Syed Ameeruddin, born on 5th December 1942 at Guntakal-A.P. (India) took his agonal last breath on November 28, 2020 in Chennai of Tamil Nadu. As a poet, critic, New College Professor and Founder of the International Poet’s Academy he earned a distinguished place among Indian English Writers of today by dint of his unfailing hard work, compositions and oeuvres. His magnum opus— Visions of Deliverance with epical grandeur explores the infinite reality in its multifarious existential dimensions ranging from mundane and temporal to the mesmerizing eternal lands of everlasting beauty signifying what in Indian lexicon is termed as Sat-Cit-Ananda— Existence, Consciousness and Bliss. The book has 30 lively poems bright like gems beaded in a string. All the poems move in a perpetual movement to create emotion, feelings of auspicious joy as at the birth of a biological being and his/her upbringing. His humanitarian concerns, philosophical backdrops, metaphysical preoccupations together solve/ resolve the chaotic realities and sparkles of life with illuminating zest and determination in a diction which applies simplicity, directness, lucidity and a lilting mode. Accordingly, he emerges as a poet with multiple hues magical and vibrant embracing verbal ecstasy, visual beauty and imagistic delicacy. Imagery and symbolism that are richly present in abundance in Ameeruddin’s poetry which has been discussed in this paper at length with appropriate citations from the text. What is more enticing to his poetry is the discovery of hitherto unfathomed secret spheres of darkness pertaining to culture, heritage and civilization. As an entertainer in poetry, he attempts to explore broader ranges of human thoughts, lived experiences, mundane, cosmic and apocalyptic visions to entertain; simultaneously to transport his discerning readers into the world of his noble creation. The subjective elements delicately connect to the events/activities of his own times. As a master craftman the poet brilliantly illustrates in his long poem the subjective imagery of his Grandson which brings to fore surrealistic and long-winded phrases. A study of all salient features such as—the artistic representation of the theme, musical texture, use of native tongue, poetic mission, prophetic utterances and lyrical grandeur has tersely been done to focus on Ameeruddin’s life and the whole gamut of his literary output with particular reference to Visions of Deliverance.

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Più fonti

Tesi sul tema "Tamil lexicon"

Sundaram, Suresh. "Lexicon-Free Recognition Strategies For Online Handwritten Tamil Words". Thesis, 2011. https://etd.iisc.ac.in/handle/2005/2363.

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In this thesis, we address some of the challenges involved in developing a robust writer-independent, lexicon-free system to recognize online Tamil words. Tamil, being a Dravidian language, is morphologically rich and also agglutinative and thus does not have a finite lexicon. For example, a single verb root can easily lead to hundreds of words after morphological changes and agglutination. Further, adoption of a lexicon-free recognition approach can be applied to form-filling applications, wherein the lexicon can become cumbersome (if not impossible) to capture all possible names. Under such circumstances, one must necessarily explore the possibility of segmenting a Tamil word to its individual symbols. Modern day Tamil alphabet comprises 23 consonants and 11 vowels forming a total combination of 313 characters/aksharas. A minimal set of 155 distinct symbols have been derived to recognize these characters. A corpus of isolated Tamil symbols (IWFHR database) is used for deriving the various statistics proposed in this work. To address the challenges of segmentation and recognition (the primary focus of the thesis), Tamil words are collected using a custom application running on a tablet PC. A set of 10000 words (comprising 53246 symbols) have been collected from high school students and used for the experiments in this thesis. We refer to this database as the ‘MILE word database’. In the first part of the work, a feedback based word segmentation mechanism has been proposed. Initially, the Tamil word is segmented based on a bounding box overlap criterion. This dominant overlap criterion segmentation (DOCS) generates a set of candidate stroke groups. Thereafter, attention is paid to certain attributes from the resulting stroke groups for detecting any possible splits or under-segmentations. By relying on feedbacks provided by a priori knowledge of attributes such as number of dominant points and inter-stroke displacements the recognition label and likelihood of the primary SVM classifier linguistic knowledge on the detected stroke groups, a decision is taken to correct it or not. Accordingly, we call the proposed segmentation as ‘attention feedback segmentation’ (AFS). Across the words in the MILE word database, a segmentation rate of 99.7% is achieved at symbol level with AFS. The high segmentation rate (with feedback) in turn improves the symbol recognition rate of the primary SVM classifier from 83.9% (with DOCS alone) to 88.4%. For addressing the problem of segmentation, the SVM classifier fed with the x-y trace of the normalized and resampled online stroke groups is quite effective. However, the performance of the classifier is not robust to effectively distinguish between many sets of similar looking symbols. In order to improve the symbol recognition performance, we explore two approaches, namely reevaluation strategies and language models. The reevaluation techniques, in particular, resolve the ambiguities in base consonants, pure consonants and vowel modifiers to a considerable extent. For the frequently confused sets (derived from the confusion matrix), a dynamic time warping (DTW) approach is proposed to automatically extract their discriminative regions. Dedicated to each confusion set, novel localized cues are derived from the discriminative region for their disambiguation. The proposed features are quite promising in improving the symbol recognition performance of the confusion sets. Comparative experimental analysis of these features with x-y coordinates are performed for judging their discriminative power. The resolving of confusions is accomplished with expert networks, comprising discriminative region extractor, feature extractor and SVM. The proposed techniques improve the symbol recognition rate by 3.5% (from 88.4% to 91.9%) on the MILE word database over the primary SVM classifier. In the final part of the thesis, we integrate linguistic knowledge (derived from a text corpus) in the primary recognition system. The biclass, bigram and unigram language models at symbol level are compared in terms of recognition performance. Amongst the three models, the bigram model is shown to give the highest recognition accuracy. A class reduction approach for recognition is adopted by incorporating the language bigram model at the akshara level. Lastly, a judicious combination of reevaluation techniques with language models is proposed in this work. Overall, an improvement of up to 4.7% (from 88.4% to 93.1%) in symbol level accuracy is achieved. The writer-independent and lexicon-free segmentation-recognition approach developed in this thesis for online handwritten Tamil word recognition is promising. The best performance of 93.1% (achieved at symbol level) is comparable to the highest reported accuracy in the literature for Tamil symbols. However, the latter one is on a database of isolated symbols (IWFHR competition test dataset), whereas our accuracy is on a database of 10000 words and thus, a product of segmentation and classifier accuracies. The recognition performance obtained may be enhanced further by experimenting on and choosing the best set of features and classifiers. Also, the word recognition performance can be very significantly improved by using a lexicon. However, these are not the issues addressed by the thesis. We hope that the lexicon-free experiments reported in this work will serve as a benchmark for future efforts.

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Sundaram, Suresh. "Lexicon-Free Recognition Strategies For Online Handwritten Tamil Words". Thesis, 2011. http://etd.iisc.ernet.in/handle/2005/2363.

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Libri sul tema "Tamil lexicon"

Madras, University of. Tamil̲p pērakarāti: Tamil̲-Tamil̲-Āṅkilam = Tamil lexicon : Tamil-Tamil-English. Chennai: Cen̲n̲aip Palkalaikkal̲akam, 2012.

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Prakasar, S. Gnana. Cor̲pir̲appu oppiyal Tamil̲ akarāti =: An etymological and comparative lexicon of the Tamil language. Chennai: Ulakat Tamil̲ārāycci Nir̲uvan̲am, 1999.

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1820-1908, Pope G. U., a cura di. Nālaṭiyār =: The Naladiyar, or, Four hundred quatrains in Tamil : with introduction, translation, and notes critical, philological, and explanatory to which is added a concordance and lexicon. New Delhi: Asian Educational Services, 1997.

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Lexicon of Tamil literature. Leiden [Netherlands]: E.J. Brill, 1995.

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Knight, Joseph, e Levi Spaulding. English and Tamil Dictionary: Or, Manual Lexicon for Schools. Giving in Tamil All Important English Words, and the Use of Many in Phrases. Creative Media Partners, LLC, 2018.

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English and Tamil Dictionary: Or, Manual Lexicon for Schools. Giving in Tamil All Important English Words, and the Use of Many in Phrases. Creative Media Partners, LLC, 2022.

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English and Tamil Dictionary: Or, Manual Lexicon for Schools. Giving in Tamil All Important English Words, and the Use of Many in Phrases. Creative Media Partners, LLC, 2022.

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Knight, Joseph, e Levi Spaulding. An English and Tamil Dictionary: Or, Manual Lexicon for Schools. Giving in Tamil All Important English Words, and the Use of Many in Phrases. Franklin Classics Trade Press, 2018.

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Lexical affinities between Tamil and Finnish: A supplement. Kuopio, Finland: H.P.A. Hakola, 2011.

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Capitoli di libri sul tema "Tamil lexicon"

Veit, Veronika. "Lodojdamba, Chadraavalyn: Tungalag Tamir". In Kindlers Literatur Lexikon (KLL), 1. Stuttgart: J.B. Metzler, 2020. http://dx.doi.org/10.1007/978-3-476-05728-0_15930-1.

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Dreyer, Dagmar. "Bardin, John Franklin: Devil Take the Blue-Tail Fly". In Kindlers Literatur Lexikon (KLL), 1–2. Stuttgart: J.B. Metzler, 2020. http://dx.doi.org/10.1007/978-3-476-05728-0_4857-1.

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Schuster, Philipp, Jonathan Immanuel Brachthäuser e Klaus Ostermann. "Region-based Resource Management and Lexical Exception Handlers in Continuation-Passing Style". In Programming Languages and Systems, 492–519. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99336-8_18.

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AbstractRegions are a useful tool for the safe and automatic management of resources. Due to their scarcity, resources are often limited in their lifetime which is associated with a certain scope. When control flow leaves the scope, the resources are released. Exceptions can non-locally exit such scopes and it is important that resources are also released in this case.Continuation-passing style is a useful compiler intermediate language that makes control flow explicit. All calls are tail calls and the runtime stack is not used. It can also serve as an implementation technique for control effects like exceptions. In this case throwing an exception means jumping to a continuation which is not the current one.How is it possible to offer region-based resource management and exceptions in the same language and translate both to continuation-passing style? In this paper, we answer this question. We present a typed language with resources and exceptions, and its translation to continuation-passing style. The translation can be defined modularly for resources and exceptions – the correct interaction between the two automatically arises from simple composition. We prove that the translation preserves well-typedness and semantics.

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"Lexicon". In Lexicon of Tamil Literature, 1–783. BRILL, 1995. http://dx.doi.org/10.1163/9789004491731_007.

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Zvelebil, Kamil V. "Abbreviations". In Lexicon of Tamil Literature, xxv. BRILL, 1995. http://dx.doi.org/10.1163/9789004491731_005.

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Zvelebil, Kamil V. "Introduction". In Lexicon of Tamil Literature, xi—xv. BRILL, 1995. http://dx.doi.org/10.1163/9789004491731_003.

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Zvelebil, Kamil V. "Preliminary Material". In Lexicon of Tamil Literature, i—vii. BRILL, 1995. http://dx.doi.org/10.1163/9789004491731_001.

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Zvelebil, Kamil V. "Foreword". In Lexicon of Tamil Literature, ix—x. BRILL, 1995. http://dx.doi.org/10.1163/9789004491731_002.

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Zvelebil, Kamil V. "Avant-propos". In Lexicon of Tamil Literature, xvii—xxiv. BRILL, 1995. http://dx.doi.org/10.1163/9789004491731_004.

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Zvelebil, Kamil V. "Handbuch der Orientalistik". In Lexicon of Tamil Literature, 784. BRILL, 1995. http://dx.doi.org/10.1163/9789004491731_008.

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Atti di convegni sul tema "Tamil lexicon"

Josepha Menandas, J., e Mary Subaja Christo. "The Revelation of Tamil Cryptographic Lexicon –Connecting Tamil Characteristics and Cubic Curve". In 2023 International Conference on Networking and Communications (ICNWC). IEEE, 2023. http://dx.doi.org/10.1109/icnwc57852.2023.10127454.

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Thavareesan, Sajeetha, e Sinnathamby Mahesan. "Sentiment Lexicon Expansion using Word2vec and fastText for Sentiment Prediction in Tamil texts". In 2020 Moratuwa Engineering Research Conference (MERCon). IEEE, 2020. http://dx.doi.org/10.1109/mercon50084.2020.9185369.

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Czarnowska, Paula, Sebastian Ruder, Edouard Grave, Ryan Cotterell e Ann Copestake. "Don’t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction". In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1090.

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