Morphological Disambiguation of Classical Sanskrit

Hellwig, Oliver

doi:10.1007/978-3-319-23980-4_3

Cited by 3 publications

(3 citation statements)

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“…Most NLP systems for SWS combine Pān . inis phonetic and morphological rules with a lexical resource, either by using formal (Huet, 2005;Goyal et al, 2009;Kulkarni and Shukla, 2009) or statistical methods, including Dirichlet processes (Natarajan and Charniak, 2011), finite state methods (Mittal, 2010), graph queries (Krishna et al, 2016) and hybrid systems (Hellwig, 2015a).…”

Section: Related Researchmentioning

confidence: 99%

Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks

Hellwig¹,

Nehrdich²

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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The paper introduces end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges (Sandhi). Tokenization of Sanskrit depends on local phonetic and distant semantic features that are incorporated using convolutional and recurrent elements. Contrary to most previous systems, our models do not require feature engineering or extern linguistic resources, but operate solely on parallel versions of raw and segmented text. The models discussed in this paper clearly improve over previous approaches to Sanskrit word segmentation. As they are language agnostic, we will demonstrate that they also outperform the state of the art for the related task of German compound splitting.

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Section: Related Researchmentioning

confidence: 99%

Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks

Hellwig¹,

Nehrdich²

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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“…Recently, Tkachenko and Sirts (2018) proposed to generate this sequence of morphological features using a neural encoder-decoder architecture. Hellwig (2016) shows a significant improvement in performance for morphological tagging in Sanskrit by using a monolithic tagset with recurrent neural network based tagging model. In systems using monolithic labels, multiple feature values pertaining to a word are combined to form a single label (Müller et al, 2013;, which leads to data sparsity for morphologically rich languages such as Czech, Turkish and Sanskrit.…”

Section: Introductionmentioning

confidence: 98%

“…The language relies heavily on morphological markers to determine the syntactic, and to some extent the semantic roles, of words in a sentence. There exist limited and partly incompatible solutions (Hellwig, 2016;Goyal and Huet, 2016;Krishna et al, 2018) for morphological tagging of Sanskrit that heavily rely on lexicon driven shallow parsers and other linguistic knowledge. However recently, neural sequential labelling models have achieved competitive results in morphological tagging for multiple languages (Cotterell and Heigold, 2017;Tkachenko and Sirts, 2018;Malaviya et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Neural Morphological Taggers for Sanskrit

Gupta¹,

Krishna²,

Goyal³

et al. 2020

Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language. As its label space can theoretically contain more than 40,000 labels, systems that explicitly model the internal structure of a label are more suited for the task, because of their ability to generalise to labels not seen during training. We find that although some neural models perform better than others, one of the common causes for error for all of these models is mispredictions due to syncretism. 1

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Machine Learning for Ancient Languages: A Survey

Sommerschield,

Assael,

Pavlopoulos

et al. 2023

Computational Linguistics

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Ancient languages preserve the cultures and histories of the past. However, their study is fraught with difficulties, and experts must tackle a range of challenging text-based tasks, from deciphering lost languages to restoring damaged inscriptions, to determining the authorship of works of literature. Technological aids have long supported the study of ancient texts, but in recent years advances in Artificial Intelligence and Machine Learning have enabled analyses on a scale and in a detail that are reshaping the field of Humanities, similarly to how microscopes and telescopes have contributed to the realm of Science. This article aims to provide a comprehensive survey of published research using machine learning for the study of ancient texts written in any language, script and medium, spanning over three and a half millennia of civilisations around the ancient world. To analyse the relevant literature, we introduce a taxonomy of tasks inspired by the steps involved in the study of ancient documents: digitisation, restoration, attribution, linguistic analysis, textual criticism, translation and decipherment. This work offers three major contributions: first, mapping the interdisciplinary field carved out by the synergy between the Humanities and Machine Learning; second, highlighting how active collaboration between specialists from both fields is key to producing impactful and compelling scholarship; third, flagging promising directions for future work in this field. Thus, this work promotes and supports the continued collaborative impetus between the Humanities and Machine Learning.

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Morphological Disambiguation of Classical Sanskrit

Cited by 3 publications

References 19 publications

Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks

Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks

Evaluating Neural Morphological Taggers for Sanskrit

Machine Learning for Ancient Languages: A Survey

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