The Experts below are selected from a list of 17064 Experts worldwide ranked by ideXlab platform
Pawan Goyal - One of the best experts on this subject based on the ideXlab platform.
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analysis of Sanskrit text parsing and semantic relations
Sanskrit Computational Linguistics, 2009Co-Authors: Pawan Goyal, Vipul Arora, Laxmidhar BeheraAbstract:In this paper, we are presenting our work towards building a dependency parser for Sanskrit language that uses deterministic finite automata(DFA) for morphological analysis and 'utsarga apavaada' approach for relation analysis. A computational grammar based on the framework of Panini is being developed. A linguistic generalization for Verbal and Nominal database has been made and declensions are given the form of DFA. Verbal database for all the class of verbs have been completed for this part. Given a Sanskrit text, the parser identifies the root words and gives the dependency relations based on semantic constraints. The proposed Sanskrit parser is able to create semantic nets for many classes of Sanskrit paragraphs(*********************). The parser is taking care of both external and internal sandhi in the Sanskrit words.
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Sanskrit Computational Linguistics - Analysis of Sanskrit Text: Parsing and Semantic Relations
Lecture Notes in Computer Science, 2009Co-Authors: Pawan Goyal, Vipul Arora, Laxmidhar BeheraAbstract:In this paper, we are presenting our work towards building a dependency parser for Sanskrit language that uses deterministic finite automata(DFA) for morphological analysis and 'utsarga apavaada' approach for relation analysis. A computational grammar based on the framework of Panini is being developed. A linguistic generalization for Verbal and Nominal database has been made and declensions are given the form of DFA. Verbal database for all the class of verbs have been completed for this part. Given a Sanskrit text, the parser identifies the root words and gives the dependency relations based on semantic constraints. The proposed Sanskrit parser is able to create semantic nets for many classes of Sanskrit paragraphs(*********************). The parser is taking care of both external and internal sandhi in the Sanskrit words.
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Sanskrit Computational Linguistics - A Study towards Design of an English to Sanskrit Machine Translation System
Lecture Notes in Computer Science, 2009Co-Authors: Pawan Goyal, R. Mahesh K. SinhaAbstract: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].
Brendan S. Gillon - One of the best experts on this subject based on the ideXlab platform.
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Sanskrit Computational Linguistics - Linguistic Investigations into Ellipsis in Classical Sanskrit
Lecture Notes in Computer Science, 2010Co-Authors: Brendan S. GillonAbstract: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.
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Sanskrit Computational Linguistics - Tagging Classical Sanskrit Compounds
Lecture Notes in Computer Science, 2008Co-Authors: Brendan S. GillonAbstract:The paper sets out a prima facie case for the claimthat the classification of Sanskrit compounds in Pāniniantradition can be retrieved from a very slight augmentation of theusual enriched context free rules.
Oliver Hellwig - One of the best experts on this subject based on the ideXlab platform.
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dating Sanskrit texts using linguistic features and neural networks
Indogermanische Forschungen, 2019Co-Authors: Oliver HellwigAbstract:Deriving historical dates or datable stratifications for texts in Classical Sanskrit, such as the epics Mahābhārata and Rāmāyaṇa, is a considerable challenge for text-historical research. This paper provides empirical evidence for subtle but noticeable diachronic changes in the fundamental linguistic structures of Classical Sanskrit, and argues that Classical Sanskrit shows enough diachronic variation for dating texts on the basis of linguistic developments. Building on this evidence, it evaluates machine learning algorithms that predict approximate dates of composition for Sanskrit texts. The paper introduces the required background, discusses the relevance of linguistic features for temporal classification, and presents a text-historical evaluation of Book 6 of the Mahābhārata, whose historical stratification is disputed in Indological research.
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Sanskrit Computational Linguistics - Performance of a Lexical and POS Tagger for Sanskrit
Lecture Notes in Computer Science, 2010Co-Authors: Oliver HellwigAbstract: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.
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Etymological trends in the Sanskrit vocabulary
Literary and Linguistic Computing, 2009Co-Authors: Oliver HellwigAbstract:The article examines how the etymological composition of the Sanskrit lexicon is influenced by time and whether this composition can be used to date Sanskrit texts automatically. For this purpose, statistical tests are applied to a corpus of lexically analyzed texts. Results reported in the article may contribute to the diachronic lexicography of Sanskrit and help to develop computational methods for analyzing anonymous and undated Sanskrit texts.
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SanskritTagger : a stochastic lexical and pos tagger for Sanskrit
2007Co-Authors: Oliver HellwigAbstract:SanskritTagger is a stochastic tagger for unpreprocessed Sanskrit text. The tagger tokenises text with a Markov model and performs part-of-speech tagging with a Hidden Markov model. Parameters for these processes are estimated from a manually annotated corpus of currently about 1.500.000 words. The article sketches the tagging process, reports the results of tagging a few short passages of Sanskrit text and describes further improvements of the program. The article describes design and function of SanskritTagger, a tokeniser and part-of-speech (POS) tagger, which analyses ”natural”, i.e. unannotated Sanskrit text by repeated application of stochastic models. This tagger has been developped during the last few years as part of a larger project for digitalisation of Sanskrit texts (cmp. (Hellwig, 2002)) and is still in the state of steady improvement. The article is organised as follows: Section 1 gives a short overview about linguistic problems found in Sanskrit texts which influenced the design of the tagger. Section 2 describes the actual implementation of the tagger. In section 3, the performance of the tagger is evaluated on short passages of text from different thematic areas. In addition, this section describes possible improvements in future versions.
R. Mahesh K. Sinha - One of the best experts on this subject based on the ideXlab platform.
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Sanskrit Computational Linguistics - A Study towards Design of an English to Sanskrit Machine Translation System
Lecture Notes in Computer Science, 2009Co-Authors: Pawan Goyal, R. Mahesh K. SinhaAbstract: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].
Laxmidhar Behera - One of the best experts on this subject based on the ideXlab platform.
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analysis of Sanskrit text parsing and semantic relations
Sanskrit Computational Linguistics, 2009Co-Authors: Pawan Goyal, Vipul Arora, Laxmidhar BeheraAbstract:In this paper, we are presenting our work towards building a dependency parser for Sanskrit language that uses deterministic finite automata(DFA) for morphological analysis and 'utsarga apavaada' approach for relation analysis. A computational grammar based on the framework of Panini is being developed. A linguistic generalization for Verbal and Nominal database has been made and declensions are given the form of DFA. Verbal database for all the class of verbs have been completed for this part. Given a Sanskrit text, the parser identifies the root words and gives the dependency relations based on semantic constraints. The proposed Sanskrit parser is able to create semantic nets for many classes of Sanskrit paragraphs(*********************). The parser is taking care of both external and internal sandhi in the Sanskrit words.
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Sanskrit Computational Linguistics - Analysis of Sanskrit Text: Parsing and Semantic Relations
Lecture Notes in Computer Science, 2009Co-Authors: Pawan Goyal, Vipul Arora, Laxmidhar BeheraAbstract:In this paper, we are presenting our work towards building a dependency parser for Sanskrit language that uses deterministic finite automata(DFA) for morphological analysis and 'utsarga apavaada' approach for relation analysis. A computational grammar based on the framework of Panini is being developed. A linguistic generalization for Verbal and Nominal database has been made and declensions are given the form of DFA. Verbal database for all the class of verbs have been completed for this part. Given a Sanskrit text, the parser identifies the root words and gives the dependency relations based on semantic constraints. The proposed Sanskrit parser is able to create semantic nets for many classes of Sanskrit paragraphs(*********************). The parser is taking care of both external and internal sandhi in the Sanskrit words.
