Vijay Sundar Ram

Also published as: R. Vijay Sundar Ram, Vijay Sundar Ram, Vijay Sundar Ram R


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Hindi to Dravidian Language Neural Machine Translation Systems
Vijay Sundar Ram | Sobha Lalitha Devi
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

Neural machine translation (NMT) has achieved state-of-art performance in high-resource language pairs, but the performance of NMT drops in low-resource conditions. Morphologically rich languages are yet another challenge in NMT. The common strategy to handle this issue is to apply sub-word segmentation. In this work, we compare the morphologically inspired segmentation methods against the Byte Pair Encoding (BPE) in processing the input for building NMT systems for Hindi to Malayalam and Hindi to Tamil, where Hindi is an Indo-Aryan language and Malayalam and Tamil are south Dravidian languages. These two languages are low resource, morphologically rich and agglutinative. Malayalam is more agglutinative than Tamil. We show that for both the language pairs, the morphological segmentation algorithm out-performs BPE. We also present an elaborate analysis on translation outputs from both the NMT systems.


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Dependency Parsing in a Morphological rich language, Tamil
Vijay Sundar Ram | Sobha Lalitha Devi
Proceedings of the First Workshop on Parsing and its Applications for Indian Languages

Dependency parsing is the process of analysing the grammatical structure of a sentence based on the dependencies between the words in a sentence. The annotation of dependency parsing is done using different formalisms at word-level namely Universal Dependencies and chunk-level namely AnnaCorra. Though dependency parsing is deeply dealt in languages such as English, Czech etc the same cannot be adopted for the morphologically rich and agglutinative languages. In this paper, we discuss the development of a dependency parser for Tamil, a South Dravidian language. The different characteristics of the language make this task a challenging task. Tamil, a morphologically rich and agglutinative language, has copula drop, accusative and genitive case drop and pro-drop. Coordinative constructions are introduced by affixation of morpheme ‘um’. Embedded clausal structures are common in relative participle and complementizer clauses. In this paper, we have discussed our approach to handle some of these challenges. We have used Malt parser, a supervised learning- approach based implementation. We have obtained an accuracy of 79.27% for Unlabelled Attachment Score, 73.64% for Labelled Attachment Score and 68.82% for Labelled Accuracy.


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Handling Noun-Noun Coreference in Tamil
Vijay Sundar Ram | Sobha Lalitha Devi
Proceedings of the WILDRE5– 5th Workshop on Indian Language Data: Resources and Evaluation

Natural language understanding by automatic tools is the vital requirement for document processing tools. To achieve it, automatic system has to understand the coherence in the text. Co-reference chains bring coherence to the text. The commonly occurring reference markers which bring cohesiveness are Pronominal, Reflexives, Reciprocals, Distributives, One-anaphors, Noun–noun reference. Here in this paper, we deal with noun-noun reference in Tamil. We present the methodology to resolve these noun-noun anaphors and also present the challenges in handling the noun-noun anaphoric relations in Tamil.


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Co-reference Resolution in Tamil Text
Vijay Sundar Ram | Sobha Lalitha Devi
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)


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How to Handle Split Antecedents in Tamil?
Vijay Sundar Ram | Sobha Lalitha Devi
Proceedings of the Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2016)


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A Hybrid Discourse Relation Parser in CoNLL 2015
Sobha Lalitha Devi | Sindhuja Gopalan | Lakshmi S. | Pattabhi RK Rao | Vijay Sundar Ram | Malarkodi C.S.
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task


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A Generic Anaphora Resolution Engine for Indian Languages
Sobha Lalitha Devi | Vijay Sundar Ram | Pattabhi RK Rao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


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Clause Boundary Identification for Malayalam Using CRF
Lakshmi S. | Vijay Sundar Ram R | Sobha Lalitha Devi
Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages


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Hybrid Approach for Coreference Resolution
Lalitha Devi Sobha | Pattabhi RK Rao | R. Vijay Sundar Ram | CS. Malarkodi | A. Akilandeswari
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task