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We formalize networks of authored arguments. These networks are then mapped to \(ASPIC{^+}\) theories that subsequently instantiate Extended Argumentation Frameworks. Evaluation of arguments in the latter determines the status of the arguments in the source networks. The methodology is illustrated through a collaboration between scholars of South Asian philosophy, logicians and formal argumentation theorists, analyzing excerpts of Sanskrit texts concerning a controversial normative debate within the philosophical school of Mīmāṃsā.

Verse translation is thriving in China today. A publishing house in Hunan has launched a whole series of foreign poetical works in Chinese translation ranging from Sanskrit Vedas to Japanese haiku and is, from all reports, doing brisk business on them. The literary journals, too, are giving generous space to poetry from abroad. A rough check of some of the Beijing-based magazines published in the last 6 months or so finds the following names among the poets translated: Margaret Atwood, Baudelaire, Robert Bly, Burns, Giosue Carducci, T. S. Eliot, Kenneth Koch, Robert Frost, Hugh MacDiarmid, Sorley Maclean, Mallarme, Yunna Moritz, Edwin Muir, Bulat Okudzhava, Giuseppe Ungaretti, Valery, Yuri Voronov, William Carlos Williams, etc.

Bīrūnī’s treatise on the rule of three and its variations, Maqāla fī rāshīkāt al-Hind, is an amalgam of Indian and Greek mathematics. He applies examples of Sanskrit origin and explains the rules based on the Greek theory of ratios. The printed Hyderabad version of the treatise is an uncritical copy of the only known manuscript, which has pages bound in an incorrect order. This is a study of the treatise, preliminary to producing a critical edition.

It is said that the Rigveda, an ancient Indian poem, contains the first reference to a prosthesis. Written in Sanskrit between 3500 and 1800 B.C., it tells the story of a warrior, Vishpala, who, having lost a leg in battle, was fitted with a metal leg so that he could continue to fight. Likewise, the Celtic god New Haw was said to have four silver fingers.

Document segmentation is one of the most important phases in machine recognition of any language. Correct segmentation of individual symbols decides the success of character recognition technique. It is used to decompose an image of a sequence of characters into sub images of individual symbols by segmenting lines and words. Devnagari is the most popular script in India. It is used for writing Hindi, Marathi, Sanskrit and Nepali languages. Moreover, Hindi is the third most popular language in the world. Devnagari documents consist of vowels, consonants and various modifiers. Hence a proper segmentation Devnagari word is challenging. A simple approach based on bounded box to segment Devnagari documents is proposed in this paper. Various challenges in segmentation of Devnagari script are also discussed.

This paper presents the application of BIS POS tagset for tagging Sanskrit. Traditionally, the number of grammatical categories for Sanskrit varies from one to five [3]. The language has been exhaustively described in the tradition. And this description is still prevalent in today’s grammar teaching. In such a situation, the application of this tagset, which is a new paradigm with respect to Sanskrit, is a challenge. In this paper, we explore how this tagset could be used in categorizing/describing the language.

In this paper we present an experiment on the use of the hierarchical Indic Languages POS Tagset (IL-POSTS) (Baskaran et al 2008 a&b), developed by Microsoft Research India (MSRI) for tagging Indian languages, for annotating Sanskrit corpus. Sanskrit is a language with richer morphology and relatively free word-order. The authors have included and excluded certain tags according to the requirements of the Sanskrit data. A revision to the annotation guidelines done for IL-POSTS is also presented. The authors also present an experiment of training the tagger at MSRI and documenting the results.

In this paper the authors present ongoing research on Sanskrit Text-to-Speech (TTS) system called ‘Samvachak’ at Special Centre for Sanskrit Studies, JNU. No TTS for Sanskrit has been developed so far. After reviewing the related research work, the paper focuses on the development of different modules of TTS System and possible challenges. The research for the TTS can be divided into two categories – TTS independent linguistic study, TTS related Research and Development (R&D). The TTS development is based on the Festival Speech Synthesis Engine.

Sanskrit and Hindi are considered structurally close owing to genealogical relations. However, on a closer look, Hindi appears to have diverged significantly more in terms of structure than in lexical ingenuities. Gender, number distinctions, ergative, postposition, verb group, double causative, echo are some (among many) remarkable structural innovations that Hindi has gone through over the ages. While the structure of Sanskrit vibhakti was fairly organized, the same may not be true for Hindi. The present paper is a study in mapping Sanskrit Noun Phrase (NP) case markers with Hindi for Machine Translation (MT) purposes with a view to evolve cross-linguistic model for Indian languages.

In this paper, we discuss the participle type of English sentences in our English to Sanskrit machine translation (EST) system. Our EST system is an integrated model of a rule based machine translation (RBMT) with artificial neural network (ANN) model which translates an English sentence into equivalent Sanskrit sentence. We use feed forward ANN for the selection of Sanskrit word like noun, verb, object, adjective etc from English to Sanskrit user data vector (UDV). Our system uses only morphological markings to identify various part of speech (POS) as well as participle type of sentences.

This paper describes about the work done in building a prototype text to speech system for Sanskrit. A basic prototype text-to-speech is built using a simplified Sanskrit phone set, and employing a unit selection technique, where prerecorded sub-word units are concatenated to synthesize a sentence. We also discuss the issues involved in building a full-fledged text-to-speech for Sanskrit.

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.

This paper presents an algorithm, which identifies different types of pronominal and its antecedents in Sanskrit, an Indo-European language. The computational grammar implemented here uses very familiar concepts such as clause, subject, object etc., which are identified with the help of morphological information and concepts such as precede and follow. It is well known that natural languages contain anaphoric expressions, gaps and elliptical constructions of various kinds and that understanding of natural languages involves assignment of interpretations to these elements. Therefore, it is only to be expected that natural language understanding systems must have the necessary mechanism to resolve the same. The method we adopt here for resolving the anaphors is by exploiting the morphological richness of the language. The system is giving encouraging results when tested with a small corpus.

Citation matching is the problem of finding which citation occurs in a given textual corpus. Most existing citation matching work is done on scientific literature. The goal of this paper is to present methods for performing citation matching on Sanskrit texts. Exact matching and approximate matching are the two methods for performing citation matching. The exact matching method checks for exact occurrence of the citation with respect to the textual corpus. Approximate matching is a fuzzy string-matching method which computes a similarity score between an individual line of the textual corpus and the citation. The Smith-Waterman-Gotoh algorithm for local alignment, which is generally used in bioinformatics, is used here for calculating the similarity score. This similarity score is a measure of the closeness between the text and the citation. The exact- and approximate-matching methods are evaluated and compared. The methods presented can be easily applied to corpora in other Indic languages like Kannada, Tamil, etc. The approximate-matching method can in particular be used in the compilation of critical editions and plagiarism detection in a literary work.

Due to the phonetic, morphological, and lexical complexity of Sanskrit, the automatic analysis of this language is a real challenge in the area of natural language processing. The paper describes a series of tests that were performed to assess the accuracy of the tagging program SanskritTagger. To our knowlegde, it offers the first reliable benchmark data for evaluating the quality of taggers for Sanskrit using an unrestricted dictionary and texts from different domains. Based on a detailed analysis of the test results, the paper points out possible directions for future improvements of statistical tagging procedures for Sanskrit.

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.

Pāṇini’s Aṣṭādhyāyī can be thought of as an automaton to generate Sanskrit words and sentences. Aṣṭādhyāyī consists of sūtras that are organized in a systematic manner. The words are derived from the roots and affixes by the application of these sūtras that follow a well defined procedure. Therefore, Aṣṭādhyāyī is best suited for computational modeling. A computational model with conflict resolution techniques was discussed by us (Sridhar et al, 2009)[12]. In continuation with that, this paper presents, an improvised computational model of Aṣṭādhyāyī. This model is further developed based on the principle of asiddhatva. A new mathematical technique called ‘filter’ is introduced to comprehensively envisage all usages of asiddhatva in Aṣṭādhyāyī.

Attempting to model Pāṇinian procedure computationally forces one to clarify concepts explicitly and allows one to test various versions and interpretations of his grammar against each other and against bodies of extant Sanskrit texts. To model Pāṇinian procedure requires creating data structures and a framework that allow one to approximate the statement of Pāṇinian rules in an executable language. Scharf (2009: 117-125) provided a few examples of how rules would be formulated in a computational model of Pāṇinian grammar as opposed to in software that generated speech forms without regard to Pāṇinian procedure. Mishra (2009) described the extensive use of attributes to track classification, marking and other features of phonetic strings. Goyal, Kulkarni, and Behera (2009, especially sec. 3.5) implemented a model of the asiddhavat section of rules (6.4.22-129) in which the state of the data passed to rules of the section is maintained unchanged and is utilized by those rules as conditions, yet the rules of the section are applied in parallel, and the result of all applicable rules applying exits the section. The current paper describes Scharf and Hyman’s implementation of rule blocking and forward-looking conditions. The former deals with complex groups of rules concerned with domains included within the scope of a general rule. The latter concerns a case where a decision at an early stage in the derivation requires evaluation of conditions that do not obtain until a subsequent stage in the derivation.

Ellipsis is a common phenomenon of Classical Sanskrit prose. No inventory of the forms of ellipsis in Classical Sanskrit has been made. This paper presents an inventory, based both on a systematic investigation of one text and on examples based on sundry reading.

The paper describes a lexical resource in the form of a relational database based indexing system for Sanskrit documents - Mahābhārata (MBh) as an example. The system is available online on http://sanskrit.jnu.ac.in/mb with input and output in Devanāgarī Unicode, using technologies such as RDBMS and Java Servlet. The system works as an interactive and multi-dimensional indexing system with search facility for MBh and has potentials for use as a generic system for all Sanskrit texts of similar structure. Currently, the system allows three types of searching facilities- ‘Direct Search’, ‘Alphabetical Search’ and ‘Search by Classes’. The input triggers an indexing process by which a temporary index is created for the search string, and then clicking on any indexed word displays the details for that word and also a facility to search that word in some other online lexical resources.

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.

Glosses and examples are the essential components of the computational lexical databases like, Wordnet. These two components of the lexical database can be used in building domain ontologies, semantic relations, phrase structure rules etc., and can help automatic or manual word sense disambiguation tasks. The present paper aims to highlight the importance of gloss in the process of WSD based on the experiences from building Sanskrit Wordnet. This paper presents a survey of Sanskrit Synonymy lexica, use of Navya-Nyāya terminology in developing a gloss and the kind of patterns evolved that are useful for the computational purpose of WSD with special reference to Sanskrit.

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.

PāniniAṣṭ ādhyā yī is often compared to a computer program for its rigour and coverage of the then prevalent Sanskrit language. The emergence of computer science has given a new dimension to the Pāninian studies as is evident from the recent efforts by Mishra [7], Hyman [5] and Scharf [10]. Ours is an attempt to discover programming concepts, techniques and paradigms employed by Pānini. We discuss how the three sūtras: pūrvatrā siddham 8.2.1, asiddhavad atrābhā t 6.4.22, and ṣatvatukor asiddhah 6.1.86 play a major role in the ordering of the sktras and provide a model which can be best described with privacy of data spaces. For conflict resolution, we use two criteria: utsarga-apavāda relation between sūtras, and the word integrity principle. However, this needs further revision. The implementation is still in progress. The current implementation of inflectional morphology to derive a speech form is discussed in detail.

The most authoritative description of the morphophonemic rules that apply at word boundaries (external sandhi) in Sanskrit is by the great grammarian Panini (fl. 5th c. B. C. E.). These rules are stated formally in Panini's grammar, the Astadhayi group of eight chapters'. The present paper summarizes Panini's handling of sandhi, his notational conventions, and formal properties of his theory. An XML vocabulary for expressing Panini's morphophonemic rules is then introduced, in which his rules for sandhi have been expressed. Although Panini's notation potentially exceeds a finite state grammar in power, individual rules do not rewrite their own output, and thus they may be automatically translated into a rule cascade from which a finite state transducer can be compiled.

This paper aims to present a way of storing Sanskrit Verbal roots in a proposed Sanskrit WordNet. The synsets of verbal roots are proposed to be created using all the available dhātupāṭhas. While doing so, it is shown that various formal as well as semantic features of the verbal roots noted by Pānini should be taken into account and stored. This will serve the purpose of disambiguation. It is also shown that verbal roots that denote a different meaning when they occur with upasargas should be stored separately and linked to the synset of the changed meaning. This feature is peculiar to Sanskrit WordNet. Since, IIT Bombay has already developed Hindi as well as Marathi WordNets, information related to storing verbal roots in these two is also presented.

We are experimenting to examine how AnglaBharati system designed to translate English to Indian languages could be adapted for translation to Sanskrit. The main contribution of our work is demonstration of machine translation of English to Sanskrit for simple sentences based on PLIL generated by AnglaBharati and A****** ***dhy*** yī rules. Presently our translation system caters to affirmative, negative, interrogative, imperative, active and passive voice sentences. In our study, we have selected a set of nouns and verbs that represent different semantic categories besides a few adverbs and adjectives. We anticipate using a number of Sanskrit resources on A****** ***dhy*** yī and morphological synthesis [2].

Pānini accounts for utterances through a derivational procedure that starts from meaning conditions involving actions, participants in actions, and other things that are related to each other. His derivational system thereby serves to form utterances of which words are a part, not isolated words that are then strung together to form utterances. Pānini's system is a continuum that starts from meaning and cooccurrence conditions that determine the introduction of affixes to bases in order to form initial strings and subsequently applies additional affixation and replacement rules to produce final strings. The system does not isolate morphology as a distinct component absolutely independent of syntax. Declensional and conjugational morphology are part of syntactic derivation, and derivational morphology is generally incorporated in the syntactic machinery. Primary derivational affixes are introduced in the course of syntactic derivation. In addition, secondary derivation affixes are regularly introduced after padas---terms that contain endings introduced in syntactic derivation---and not after mere bases in a separate morphological component. Further, composition takes place within the context of syntactic derivation; compounds are formed from related padas of initial strings, which involve number distinctions. Even the formation of certain items with feminine suffixes takes place within the syntactic machinery and not in a totally separate lexicon. Thus Pānini's derivational system is an integrated system accounting for utterances of Sanskrit; it lacks a sharp dichotomy between what western grammarians call syntax and morphology.

With advances in the field of digitization of printed documents and several mass digitization projects underway, information retrieval and document search have emerged as key research areas. However, most of the current work in these areas is limited to English and a few oriental languages. The lack of efficient solutions for Indic scripts and languages such as Sanskrit has hampered information extraction from a large body of documents of cultural and historical importance. This chapter presents two relevant topics in this area. First, we describe the use of a script specific Keyword Spotting for Sanskrit documents that makes use of domain knowledge of the script. Second, we address the needs of a digital library to provide access to a collection of documents from multiple scripts. This requires intelligent solutions which scale across different scripts. We present a script independent Keyword Spotting approach for this purpose. Experimental results illustrate the efficacy of our methods.

We discuss the mathematical structure of various levels of representation of Sanskrit text in order to guide the design of computer aids aiming at useful processing of the digitalised Sanskrit corpus. Two main levels are identified, respectively called the linear and functional level. The design space of these two levels is sketched, and the computational implications of the main design choices are discussed. Current solutions to the problems of mechanical segmentation, tagging, and parsing of Sanskrit text are briefly surveyed in this light. An analysis of the requirements of relevant linguistic resources is provided, in view of justifying standards allowing inter-operability of computer tools. This paper does not attempt to provide definitive solutions to the representation of Sanskrit at the various levels. It should rather be considered as a survey of various choices, allowing an open discussion of such issues in a formally precise general framework.

Pānini's grammar is widely known for its formal treatment of the Sanskrit language. Many scholars Jha 2004 have earlier taken a systemic view of Pānini and have argued that Pānini's system is easily implementable. However, on a closer look, several complications arise, especially in Pānini's recourse to semantics in many of the vidhi and saṁjnā. rules. This seems to happen more in the kāraka prakaraa than in other components. The authors of this paper have highlighted the challenges in implementing some of the semantic aspects of Pānini's kāraka system. For example, the semantic conditions of being most desired by the agent (karturīpsitatamam ) and of being most effective (sādhakatamam ) are difficult to formalize in the rules specifying the semantic conditions for an object's being termed karman and karana respectively. Likewise, the semantic conditions of being that which the agent approaches with the direct object (karmanā yamabhipraiti ), and being the one pleased in relation to verbs of pleasing (rucyarthānā m prīyamā nah ) are difficult to formalize in rules specifying semantic conditions for an object's being termed sampradāna. The paper also looks at possible strategies to handle such situations, and presents pseudo-code like translations of some kāraka rules.

In this paper we investigate the use of computational tools in the textual analysis of Sanskrit manuscripts. We use the maximum chi-squared method, a technique borrowed from molecular biology, to analyse the distribution of variants in two different pairs of manuscripts of the Dyūtaparvan . This method gives some insight into the process of manuscript copying in the Mah***bh*** rata tradition. In particular it provides evidence that at least one of the scribes used more than one exemplar to produce his own version of the Mah***bh*** rata .

In this paper an attempt is made to study the problem of overgeneration that is caused by the application of the system of P******ini The system of P******ini is made up of certain rules stated by him and his commentators namely, K***ty***yana and Patanjali . These rules are supposed to produce the forms that are used in the language, i.e. Sanskrit. However, sometimes the technical application of these rules produces such forms which are not actually used in the language. In fact, sometimes it is beyond human capacities to use such forms. In the present paper two such cases dealing with the phonological overgeneration are studied and possible solutions are proposed to avoid the problem.

The paper describes a Sanskrit morphological analyzer that identifies and analyzes inflected noun-forms and verb-forms in any given sandhi-free text. The system which has been developed as java servlet RDBMS can be tested at http://sanskrit.jnu.ac.in (Language Processing Tools > Sanskrit Tinanta Analyzer/Subanta Analyzer) with Sanskrit data in Unicode text. Subsequently, the separate systems of subanta and tinanta will be combined into a single system of sentence analysis with karaka interpretation. Currently, the system checks and labels each word as three basic POS categories - subanta, tinanta, and avyaya. Thereafter, each subanta is sent for subanta processing based on an example database and a rule database. The verbs are examined based on a database of verb roots and forms as well by reverse morphology based on Paninian techniques. Future enhancements include plugging in the amarakosa (http://sanskrit.jnu.ac.in/amara) and other noun lexicons with the subanta system. The tinanta will be enhanced by the kṛdanta analysis module being developed separately.

Professor Joshi proposes that a phonemic encoding scheme be adopted as the standard for machine processing of Sanskrit text. In the scheme he details, each phoneme is represented by a single character code that represents a single Sanskrit sound. Graphic units in Devanagari corresponding to syllabic units, including consonant plus /a/, are represented as sequences. Glyphs corresponding to intial vowels verses dependent vowels are not given distinct encodings; rather they are selected based upon context. (P. Scharf)

The development of a machine translation system needs that we identify the patterns of divergence between two languages. Though a number of MT developers have given attention to this problem, it is difficult to derive general strategies which can be used for any language pair. Therefore, further exploration is always needed to identify different sources of translation divergence in different pairs of translation languages. In this paper, we discuss translation pattern between English-Sanskrit and Hindi-Sanskrit of various constructions to identify the divergence in English-Sanskrit-Hindi language pairs. This will enable us to come up with strategies to handle these situations and coming up with correct translation. The base has been the classification of translation divergence presented by Dorr [Dorr, 1994].