Amba Kulkarni

Sanskrit Compound Processor

Sanskrit is very rich in compound formation. Typically a compound does not code the relation between its components explicitly. To understand the meaning of a compound, it is necessary to identify its components, discover the relations between them and finally generate a paraphrase of the compound. In this paper, we discuss the automatic segmentation and type identification of a compound using simple statistics that results from the manually annotated data.

Designing a Constraint Based Parser for Sanskrit

Verbal understanding (śā bdabodha) of any utterance requires the knowledge of how words in that utterance are related to each other. Such knowledge is usually available in the form of cognition of grammatical relations. Generative grammars describe how a language codes these relations. Thus the knowledge of what information various grammatical relations convey is available from the generation point of view and not the analysis point of view. In order to develop a parser based on any grammar one should then know precisely the semantic content of the grammatical relations expressed in a language string, the clues for extracting these relations and finally whether these relations are expressed explicitly or implicitly. Based on the design principles that emerge from this knowledge, we model the parser as finding a directed Tree, given a graph with nodes representing the words and edges representing the possible relations between them. Further, we also use the Mīmā ṃsā constraint of ākā ṅkṣā (expectancy) to rule out non-solutions and sannidhi (proximity) to prioritize the solutions. We have implemented a parser based on these principles and its performance was found to be satisfactory giving us a confidence to extend its functionality to handle the complex sentences.

The Knowledge Structure in Amarakosa

Amarakośa is the most celebrated and authoritative ancient thesaurus of Sanskrit. It is one of the books which an Indian child learning through Indian traditional educational system memorizes as early as his first year of formal learning. Though it appears as a linear list of words, close inspection of it shows a rich organisation of words expressing various relations a word bears with other words. Thus when a child studies Amarakośa further, the linear list of words unfolds into a knowledge web. In this paper we describe our effort to make the implicit knowledge in Amarakośa explicit. A model for storing such structure is discussed and a web tool is described that answers the queries by reconstructing the links among words from the structured tables dynamically.

Vibhakti Divergence between Sanskrit and Hindi

Translation divergence at various levels between languages arises due to the different conventions followed by different languages for coding the information of grammatical relations. Though Sanskrit and Hindi belong to the same Indo-Aryan family and structurally as well as lexically Hindi inherits a lot from Sanskrit, yet divergences are observed at the level of function words such as vibhaktis. Pāṇini in his Aṣṭādhyāyī has assigned a default vibhakti to kārakas alongwith many scopes for exceptions. He handles these exceptions either by imposing a new kāraka role or by assigning a special vibhakti. However, these methods are not acceptable in Hindi in toto. Based on the nature of deviation, we propose seven cases of divergences in this paper.

Computer Simulation of Aṣṭ ādhyā yī : Some Insights

Monoclonal antibodies which react specifically with a complementary rheumatic fever associated antigen on human B-lymphocytes derived from hybridoma cell line HB8783, process for preparing such antibodies, the use of antibodies for detecting rheumatic fever in mammals and test kit containing the antibodies.

AnnCorra: building tree-banks in Indian languages

This paper describes a dependency based tagging scheme for creating tree banks for Indian languages. The scheme has been so designed that it is comprehensive, easy to use with linear notation and economical in typing effort. It is based on Paninian grammatical model.

Constituency Parsing of Complex Noun Sequences in Hindi

A complex noun sequence is one in which a head noun is recursively modified by one or more bare nouns and/or genitives Constituency analysis of complex noun sequence is a prerequisite for finding dependency relation semantic relation between components of the sequence. Identification of dependency relation is useful for various applications such as question answering, information extraction, textual entailment, paraphrasing. In Hindi, syntactic agreement rules can handle to a large extent the parsing of recursive genitives Sharma, 2012[12].This paper implements frequency based corpus driven approaches for parsing recursive genitive structures that syntactic rules cannot handle as well as recursive compound nouns and combination of gentive and compound noun sequences. Using syntactic rules and dependency global algorithm, an accuracy of 92.85% is obtained.

Later Nyāya Logic: Computational Aspects

In this article we describe the computational aspects of the Technical Language of Navya-Nyāya. Navya-Nyaya is an off-shoot of the early Nyāya philosophy. It deviates from the Nyāya philosophy in three major ways. First, instead of prameyas the discussions are centered around the pramāṇas. Second, the Navya-Nyāya has adapted the Vaiseṣika ontology. And finally it has introduced a few concepts expressed through an unambiguous technical terminology that brings in a clarity in the communication removing the inherent ambiguities of a natural language. In this article the syntax of expressions involving this technical terminology is described, followed by a scheme based on Conceptual Graphs of Sowa for their graphical rendering. Finally a computational algorithm is described that renders the graphs corresponding to the Navya-Nyāya expressions semi-automatically. A. Kulkarni (*) Department of Sanskrit Studies, University of Hyderabad, Hyderabad, Telangana, India # Springer (India) Pvt. Ltd. 2018 S. Sarukkai (ed.), Handbook of Logical Thought in India, https://doi.org/10.1007/978-81-322-1812-8_12-1 1