Relevant bibliographies by topics / Malayalam languages

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Malayalam languages'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Malayalam languages"

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K. Bijimol, T., and John T. Abraham. "A Rule Based Approach for Translation of Causative Construction of English and Malayalam for the Development of Prototype for Malayalam to English and English To Malayalam Bilingual Machine Translation System." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 542. http://dx.doi.org/10.14419/ijet.v7i4.36.24134.

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Malayalam is one of the Indian languages and it is a highly agglutinative and morphologically rich. These linguistic specialties of Malayalam determine the quality of all kinds of Malayalam machine translation systems. Causative sentences translations in Malayalam to English and English to Malayalam were analysed using Google Translation System and identified that causative sentence translation in these languages is not up to the mark. This paper discusses the concept and method of causative sentence handling in Malayalam to English and English to Malayalam Machine Translation Systems. A Rule-based system is proposed here to handle the causative sentence in both languages.
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Sebastian, Mary Priya, and G. Santhosh Kumar. "Verb Phrases Alignment Technique for English-Malayalam Parallel Corpus in Statistical Machine Translation Special issue on MTIL 2017." Journal of Intelligent Systems 28, no. 3 (July 26, 2019): 479–92. http://dx.doi.org/10.1515/jisys-2018-0066.

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Abstract Machine translation (MT) from English to foreign languages is a fast developing area of research, and various techniques of translation are discussed in the literature. However, translation from English to Malayalam, a Dravidian language, is still in the rising stage, and works in this field have not flourished to a great extent, so far. The main reason of this shortcoming is the non-availability of linguistic resources and translation tools in the Malayalam language. A parallel corpus with alignment is one of such resources that are essential for a machine translator system. This paper focuses on a technique that enables automatic setting up of a verb-aligned parallel corpus by exploring the internal structure of the English and Malayalam language, which in turn facilitates the task of machine translation from English to Malayalam.
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Menon, A. Govindankutty. "Some observations on the sub-group Tamil-Malayalam: differential realizations of the cluster *ṉt." Bulletin of the School of Oriental and African Studies 53, no. 1 (February 1990): 87–99. http://dx.doi.org/10.1017/s0041977x00021285.

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The languages belonging to the South-Dravidian sub-group of the Dravidian languages exhibit various degrees of genetic relationship. Though the common retentions and the later innovations have led to an indisputable consensus on broad issues, details regarding the finer genetic relationship remain to be worked out. This paper tries to analyse the genetic relationship between Tamil and Malayalam in the light of some sound changes, and also discusses the problems involved in the interpretation of the linguistic material.All the comparative evidence points to a closer relationship between Tamil and Malayalam. They preserve alike some of the Proto-South-Dravidian features such as *ḻ, *ṛṛ besides innovating sound changes such as *k– > c–. On the basis of a number of common phonological and morphological features, scholars concluded that at the stage known as late Old and early Middle Tamil, Tamil and Malayalam were most probably basically one language with pre- Malayalam as a diverging western dialect of the spoken form of that common language. The innovations which appeared in the ninth- and tenth-century West Coast inscriptions were linked with the similar tendencies observed in colloquial Tamil. The oldest Malayalam inscriptions and literary texts are not earlier than about the ninth century A.D. Since this period is contemporaneous with Middle Tamil, it was argued that the development of a separate language had to be dated to that period.
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Premjith, B., and K. P. Soman. "Deep Learning Approach for the Morphological Synthesis in Malayalam and Tamil at the Character Level." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (November 30, 2021): 1–17. http://dx.doi.org/10.1145/3457976.

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Morphological synthesis is one of the main components of Machine Translation (MT) frameworks, especially when any one or both of the source and target languages are morphologically rich. Morphological synthesis is the process of combining two words or two morphemes according to the Sandhi rules of the morphologically rich language. Malayalam and Tamil are two languages in India which are morphologically abundant as well as agglutinative. Morphological synthesis of a word in these two languages is challenging basically because of the following reasons: (1) Abundance in morphology; (2) Complex Sandhi rules; (3) The possibilty in Malayalam to form words by combining words that belong to different syntactic categories (for example, noun and verb); and (4) The construction of a sentence by combining multiple words. We formulated the task of the morphological generation of nouns and verbs of Malayalam and Tamil as a character-to-character sequence tagging problem. In this article, we used deep learning architectures like Recurrent Neural Network (RNN) , Long Short-Term Memory Networks (LSTM) , Gated Recurrent Unit (GRU) , and their stacked and bidirectional versions for the implementation of morphological synthesis at the character level. In addition to that, we investigated the performance of the combination of the aforementioned deep learning architectures and the Conditional Random Field (CRF) in the morphological synthesis of nouns and verbs in Malayalam and Tamil. We observed that the addition of CRF to the Bidirectional LSTM/GRU architecture achieved more than 99% accuracy in the morphological synthesis of Malayalam and Tamil nouns and verbs.
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Varghese, Simi. "Remapping the Visual Contours: An Enquiry into the Film Narratives of Adoor Gopalakrishnan." Asian Review of Social Sciences 8, S1 (February 5, 2019): 96–100. http://dx.doi.org/10.51983/arss-2019.8.s1.2778.

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Adoor Gopalakrishan has been the greatest film director who had elevated Malayalam film to the level of World Cinema. Truly, he is the master craftsman of Indian cinema second only to Satyajit Ray. He had discovered the identity of Malayalam through his visual narratives. He had metamorphosed each film as an experience and eked out a new visual repertoire for Malayalam films. Hitherto, no serious study has been conducted to absorb the visual magnificence of Adoor films. Concerted efforts have been initiated in other Indian languages and world languages to trace the visual dynamics employed in Adoor films. When foreign film critics approach his films seriously, even today we often falter to imbibe the film sensitivity and culture kickstarted by Mr Adoor Gopalakrishnan. Still, he is the ‘unravished fragrance’ of Malayalam film industry. Adoor has been truly one of the masters of world cinema and had carved a special niche for him in the global film map. My paper tries to portray the new visual fervor inculcated by Adoor films in the Malayalam psyche and will unravel the subtle nuances which deeply touch the labyrinthine milieu of Malayalam film world.
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Mathew, Mili Mary, and Jayashree S. Bhat. "ASPECTS OF EMOTIONAL PROSODY IN MALAYALAM AND HINDI." Buckingham Journal of Language and Linguistics 3 (September 16, 2010): 25–34. http://dx.doi.org/10.5750/bjll.v3i0.22.

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Emotional prosody is considered as the ability to express emotions. Intonation is one parameter of prosody that gives information on the production aspects of emotions. The aim was to study the intonation patterns in two languages, Malayalam and Hindi, from two different language groups in India and also to document if there are differences in the patterns produced across gender groups. Eight native speakers of Malayalam and Hindi, in the age range of 18-40 years were considered for the study (two males and two females for each of the languages). Simple sentences with five basic emotions were used as the stimuli and the samples were recorded in Motor Speech Profile software (MSP) of Computerized Speech Lab 4150. The patterns were plotted using the PHH model. Acoustic data were subjected to statistical analysis, using Mann Whitney U Test (SPSS Version 16).The results of this study reveal that across the five emotions, the terminal intonation pattern has a falling contour, except for the emotion of anger in females, which has a raising contour. This was observed in both the languages. On subjective observation, there were instances of differences in the patterns across the genders, but these were not statistically significant.
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Mohan, Dhanya, and Sandeep Maruthy. "Vowel Context Effect on the Perception of Stop Consonants in Malayalam and Its Role in Determining Syllable Frequency." Journal of Audiology and Otology 25, no. 3 (July 10, 2021): 124–30. http://dx.doi.org/10.7874/jao.2021.00087.

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Background and Objectives: The study investigated vowel context effects on the perception of stop consonants in Malayalam. It also probed into the role of vowel context effects in determining the frequency of occurrence of various consonant-vowel (CV) syllables in Malayalam.Subjects and Methods: The study used a cross-sectional pre-experimental post-test only research design on 30 individuals with normal hearing, who were native speakers of Malayalam. The stimuli included three stop consonants, each spoken in three different vowel contexts. The resultant nine syllables were presented in original form and five gating conditions. The consonant recognition in different vowel contexts of the participants was assessed. The frequency of occurrence of the nine target syllables in the spoken corpus of Malayalam was also systematically derived.Results: The consonant recognition score was better in the /u/ vowel context compared with /i/ and /a/ contexts. The frequency of occurrence of the target syllables derived from the spoken corpus of Malayalam showed that the three stop consonants occurred more frequently with the vowel /a/ compared with /u/ and /i/.Conclusions: The findings show a definite vowel context effect on the perception of the Malayalam stop consonants. This context effect observed is different from that in other languages. Stop consonants are perceived better in the context of /u/ compared with the /a/ and /i/ contexts. Furthermore, the vowel context effects do not appear to determine the frequency of occurrence of different CV syllables in Malayalam.
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C, Sunitha, A. Jaya, and Amal Ganesh. "Automatic summarization of Malayalam documents using clause identification method." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 4929. http://dx.doi.org/10.11591/ijece.v9i6.pp4929-4938.

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<span>Text summarization is an active research area in the field of natural language processing. Huge amount of information in the internet necessitates the development of automatic summarization systems. There are two types of summarization techniques: Extractive and Abstractive. Extractive summarization selects important sentences from the text and produces summary as it is present in the original document. Abstractive summarization systems will provide a summary of the input text as is generated by human beings. Abstractive summary requires semantic analysis of text. Limited works have been carried out in the area of abstractive summarization in Indian languages especially in Malayalam. Only extractive summarization methods are proposed in Malayalam. In this paper, an abstractive summarization system for Malayalam documents using clause identification method is proposed. As part of this research work, a POS tagger and a morphological analyzer for Malayalam words in cricket domain are also developed. The clauses from input sentences are identified using a modified clause identification algorithm. The clauses are then semantically analyzed using an algorithm to identify semantic triples - subject, object and predicate. The score of each clause is then calculated by using feature extraction and the important clauses which are to be included in the summary are selected based on this score. Finally an algorithm is used to generate the sentences from the semantic triples of the selected clauses which is the abstractive summary of input documents.</span>
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Sivan, Remya. "PROCESSING OF ENGLISH-MALAYALAM CODE MIXED LANGUAGES USING NLP TECHNIQUES." International Journal of Technical Research & Science 5, no. 6 (June 15, 2020): 16–19. http://dx.doi.org/10.30780/ijtrs.v05.i06.004.

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Premjith, B., M. Anand Kumar, and K. P. Soman. "Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus." Journal of Intelligent Systems 28, no. 3 (July 26, 2019): 387–98. http://dx.doi.org/10.1515/jisys-2019-2510.

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Abstract Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus and Indian languages are morphologically rich. In this paper, we propose a neural machine translation (NMT) system for four language pairs: English–Malayalam, English–Hindi, English–Tamil, and English–Punjabi. We also collected sentences from different sources and cleaned them to make four parallel corpora for each of the language pairs, and then used them to model the translation system. The encoder network in the NMT architecture was designed with long short-term memory (LSTM) networks and bi-directional recurrent neural networks (Bi-RNN). Evaluation of the obtained models was performed both automatically and manually. For automatic evaluation, the bilingual evaluation understudy (BLEU) score was used, and for manual evaluation, three metrics such as adequacy, fluency, and overall ranking were used. Analysis of the results showed the presence of lengthy sentences in English–Malayalam, and the English–Hindi corpus affected the translation. Attention mechanism was employed with a view to addressing the problem of translating lengthy sentences (sentences contain more than 50 words), and the system was able to perceive long-term contexts in the sentences.
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Dissertations / Theses on the topic "Malayalam languages"

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Radhakrishnan, Sreedivya. "Perception of synthetic vowels by monolingual and bilingual Malayalam speakers." [Kent, Ohio] : Kent State University, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=kent1258953613.

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Thesis (Ph.D.)--Kent State University, 2009.
Title from PDF t.p. (viewed May 17, 2010). Advisor: John Hawks. Keywords: Speech perception; Vowels; Malayalam; Second Language. Includes bibliographical references (p. 198-216).
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Moore, David L. ""I Don't Speak My Own Language": Ethnicity Among the Malayalees of Singapore." PDXScholar, 1994. https://pdxscholar.library.pdx.edu/open_access_etds/4773.

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This thesis is an ethnographic examination of the significance of Malayalee ethnicity in Singapore. Ethnic identity is important in the daily lives of Singaporeans, due in part to the government-directed public focus in Singapore on the ideal of multiculturalism through which it is asserted that to be Singaporean, one must be, in the main, Chinese, Malay, or Indian. But other identities, such as Malayalee, a subset of the larger category "Indian", have not decreased in importance. They, in fact, remain important in identifying what kind of Chinese, Malay, or Indian a person is, as Chinese, Malay, or Indian identifies what kind of Singaporean someone is. In the thesis I focus on a core contradiction in Singapore Malayalee culture. In Singapore it is perceived as very important to know one's 'mother tongue' in order to know one's culture and heritage. But Malayalees growing up in Singapore have not had much chance to learn their language, Malayalam, nor have they had much practical use for it outside of the home. Therefore, many Singapore Malayalees feel a sense of alienation from Malayalee culture. Many feel they know little about their own culture because they do not speak their own language. With the emphasis on multiculturalism the sense of a distinctive Malayalee culture will remain in Singapore, as will the sense of alienation from it felt by many Malayalees. In the analysis practice theory and the concept of habitus are used to identify how people's actions have been affected by particular historical circumstances, and how their actions have, in turn, structured the form of Malayalee ethnicity in Singapore today. It is asserted that practice theory gives a much better explanation of Singapore Malayalee ethnicity than primordialist or instrumentalist theories. There have been only five previous studies of Singapore Malayalees, all Bachelor's Honors theses at the National University of Singapore. This study contributes, therefore, to a sparse literature.
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Narasimhan, Kidambi Rama. "Coronals, velars and front vowels." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23728.

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In this thesis, we investigate several processes affecting coronals and velars in Tamil and Malayalam, two Dravidian languages spoken in southern India. We begin by discussing two assimilation processes which apply adjacent to front vowels, Palatalization, where anterior coronals become palatoalveolar, and Coronalization, where velars are fronted to palatoalveolar. We compare and contrast the feature geometries proposed by Sagey (1986) and Hume (1992) in their ability to adequately express these processes. In Sagey's model, front vowels are argued to be Dorsal. It is thus impossible to express either Palatalization or Coronalization as spreading. In Hume's model, where front vowels are Coronal, both processes involve spreading. However, the model does not formally distinguish between these two processes across languages; thus, it fails to capture the fact that Palatalization is widely attested but Coronalization seems to be restricted to diachronic alternations. In order to express this asymmetry, we adopt the model advanced by Goad & Narasimhan (1994), a revision of Goad (1993), where Palatalization involves spreading but Coronalization is a two-step process, spreading followed by reanalysis. In this model, a single feature (front), defined as "front of articulator", is doubly dependent on both Dorsal and Coronal nodes. Its interpretation is thus partly determined by the node to which it links; it marks apicality in coronals and front of tongue body in dorsals. In Chapter 3, we demonstrate how this model allows us to capture the fact that in Malayalam, only a subset of the anterior coronal consonants, the apicals, form a natural class with front vowels. In Chapter 4, we provide support for the model from languages other than Tamil and Malayalam, both Dravidian and non-Dravidian.
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Books on the topic "Malayalam languages"

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India), KIRTADS (Organization :. Dictionary of tribal languages. Kozhikode: Kerala Institute for Research Training and Development Studies of Scheduled Castes and Scheduled Tribes, 2012.

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Parametric studies in Malayalam syntax. New Delhi: Allied Publishers, 1999.

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Pi, Śaṅkuṇṇināyar Eṃ. Points of contact between Prakrit and Malayalam. Thiruvananthapuram: DLA Publications, 1995.

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Ī, Viśvanātha Ayyara Ena. Anuvāda: Bhāshāem̐, samasyāem̐. Trivendrama: Svāti Prakāśana, 1986.

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South Indian scripts in Sanskrit manuscripts and prints: Grantha Tamil-Malayalam-Telugu-Kannada-Nandinagari. Wiesbaden: Harrassowitz, 2001.

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Munṣi, O. Ke. Taddhitakōśaṃ. Kōl̲ikkōṭ: Mātr̥bhūmi Pr̲int̲iṅg ānḍ Pabḷiṣiṅg Kampani, 1988.

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An international bibliography of Dravidian languages and linguistics. Madras: T.R. Publications, 1994.

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Satyanārāyaṇa, Mo. Hindī Malayālama svabodhinī =: Hindī Malayāḷam svabōdhini. Madrāsa: Dakshiṇa Bhārata Hindī Pracāra Sabhā, 1987.

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Vijayakumāran, Si Pi Vi. Pr̲āyōgikavivarttana vicintanaṃ: Paṭhanaṃ. [Kannannatt, Kerala]: Vijayakumaran C.P.V., 2000.

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Ayyara, Ena Ī. Viśvanātha. Vivarttanavicāraṃ. Tiruvanatapuraṃ: Kēraḷa Bhāṣā Inst̲it̲t̲ūṭṭ, 2014.

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Book chapters on the topic "Malayalam languages"

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Rubin, Aaron D., and Lily Kahn. "Malayalam." In Jewish Languages from A to Z, 135–38. New York : Routledge, [2021]: Routledge, 2020. http://dx.doi.org/10.4324/9781351043441-27.

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Rajendran, S., and K. P. Soman. "Malayalam WordNet." In The WordNet in Indian Languages, 119–45. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1909-8_7.

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Ravindra Kumar, R., K. G. Sulochana, and T. R. Indhu. "Online Handwriting Recognition for Malayalam Script." In Information Systems for Indian Languages, 199–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19403-0_32.

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Sreekumar, P., and S. Prema. "Morphosyntax of expressives in Malayalam." In Expressive Morphology in the Languages of South Asia, 135–49. New York : Routledge, 2020.: Routledge, 2020. http://dx.doi.org/10.4324/9781315265629-5.

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Jayaseelan, K. A. "The Serial Verb Construction in Malayalam." In Clause Structure in South Asian Languages, 67–91. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2719-2_3.

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Kumar, R. Ravindra, K. G. Sulochana, and V. Jayan. "Computational Aspect of Verb Classification in Malayalam." In Information Systems for Indian Languages, 15–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19403-0_3.

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Mohanan, K. P. "Malayalam Phonology: Segmentals." In Studies in Natural Language and Linguistic Theory, 63–108. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-3719-2_3.

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Mohanan, K. P. "Malayalam Phonology: Suprasegmentals." In Studies in Natural Language and Linguistic Theory, 109–26. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-3719-2_4.

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Fernandez, Pilar Chamorro, and Lindsey Antonini. "Quantification in Malayalam." In Handbook of Quantifiers in Natural Language: Volume II, 453–515. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-44330-0_9.

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Jayaseelan, K. A. "11. The possessor — experiencer dative in Malayalam." In Typological Studies in Language, 227. Amsterdam: John Benjamins Publishing Company, 2004. http://dx.doi.org/10.1075/tsl.60.13jay.

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Conference papers on the topic "Malayalam languages"

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Nandy, Paromita. "Ratiocinate the Sociocultural Habits of Bengali Diaspora Residing in Kerala: A Linguistic Anthropology Study." In GLOCAL Conference on Asian Linguistic Anthropology 2019. The GLOCAL Unit, SOAS University of London, 2019. http://dx.doi.org/10.47298/cala2019.6-2.

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The paper alludes to the study of how humans relocate themselves with cultural practice and its particular axiom, which embrace the meaning and value of how material and intellectual resource are embedded in culture. The study stimulates the cultural anthropology of the Bengali (Indo-Aryan, Eastern India) diaspora in Kerala (South India) that is dynamic and which keeps changing with the environment, keeping in mind a constant examination of group rituals, traditions, eating habits and communication. Languages are always in a state of flux, as are societies, and society contains customs and practices, beliefs, attitudes, way of life and the way people organize themselves as a group. The study scrutinizes the relationship between language and culture of Bengali people while fraternizing with Malayalee which encapsulates cultural knowledge and locates this in the interactions among members of varied cultural groups across time and space. This is influenced by that Bengali diasporic people change across generations owing to cultural gaps and remodeling of language and culture. The study investigates how a social group, having different cultural habits, manages time and space of a new and diverse sociopolitical situation. Moreover, it also investigates the language behaviour of the Bengali diaspora in Kerala by analyzing the linguistic features of Malayalam (Dravidian) spoken, such as how they express their cultural codes in different spatiotemporal conditions and their lexical choice in those situations.
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Nair, Latha R., and David peter S. "Language Parsing and Syntax of Malayalam Language." In 2nd International Symposium on Computer, Communication, Control and Automation. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/3ca-13.2013.59.

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Vasudev, Drisya, and K. K. Anish Babu. "Speaker identification using FBCC in Malayalam language." In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2014. http://dx.doi.org/10.1109/icacci.2014.6968656.

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James, Jesin, and Deepa P. Gopinath. "Modeling pause duration for Malayalam language TTS." In 2014 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, 2014. http://dx.doi.org/10.1109/icalip.2014.7009831.

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Ashna, M. P., and Ancy K. Sunny. "Lexicon based sentiment analysis system for malayalam language." In 2017 International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2017. http://dx.doi.org/10.1109/iccmc.2017.8282571.

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Deekshitha, G., K. R. Sreelakshmi, Ben P. Babu, and Leena Mary. "Development of Spoken Story Database in Malayalam Language." In 2018 4th International Conference on Electrical Energy Systems (ICEES). IEEE, 2018. http://dx.doi.org/10.1109/icees.2018.8442342.

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Gokul, P. P., B. K. Akhil, and Kumar K. M. Shiva. "Sentence similarity detection in Malayalam language using cosine similarity." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2017. http://dx.doi.org/10.1109/rteict.2017.8256590.

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Nair, Latha R., and David Peter S. "Shallow parser for Malayalam language using finite state cascades." In 2011 4th International Congress on Image and Signal Processing (CISP). IEEE, 2011. http://dx.doi.org/10.1109/cisp.2011.6100786.

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Rojan, Annlin, Edwin Alias, Georgy M. Rajan, Jithin Mathew, and Dhanya Sudarsan. "Natural Language Processing based Text Imputation for Malayalam Corpora." In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2020. http://dx.doi.org/10.1109/icesc48915.2020.9156036.

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Anisha Aziz T and Sunitha C. "A hybrid Parts Of Speech tagger for Malayalam language." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275825.

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