Language Technology for Digital Linguistics: Turning the Linguistic Survey of India into a Rich Source of Linguistic Information

Borin, Lars; Virk, Shafqat Mumtaz; Saxena, Anju

doi:10.1007/978-3-319-77113-7_42

Cited by 5 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent initiative to digitise old linguistic data is the digitisation of the Linguistic Survey of India (Grierson, 1903(Grierson, -1928 under the project South Asia as a linguistic area? Exploring big-data methods in areal and genetic linguistics (Borin et al, 2020(Borin et al, , 2018(Borin et al, , 2014. Using OCR and subsequent information extraction from the text, Borin et al have shown that "old" data still has much to tell for the computational study of typology and comparative linguistics.…”

Section: Text Digitisation and Ocrmentioning

confidence: 99%

Computational historical linguistics and language diversity in South Asia

Arora¹,

Farris²,

Basu³

et al. 2022

Preprint

View full text Add to dashboard Cite

South Asia is home to a plethora of languages, many of which severely lack access to new language technologies. This linguistic diversity also results in a research environment conducive to the study of comparative, contact, and historical linguistics-fields which necessitate the gathering of extensive data from many languages. We claim that data scatteredness (rather than scarcity) is the primary obstacle in the development of South Asian language technology, and suggest that the study of language history is uniquely aligned with surmounting this obstacle. We review recent developments in and at the intersection of South Asian NLP and historical-comparative linguistics, describing our and others' current efforts in this area. We also offer new strategies towards breaking the data barrier.

show abstract

Section: Text Digitisation and Ocrmentioning

confidence: 99%

Computational historical linguistics and language diversity in South Asia

Arora¹,

Farris²,

Basu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Previously, a few experimental techniques and associated systems have been reported for automatic extraction of typological information. In (Borin et al, 2018;Virk et al, 2017), the authors have reported on simple pattern matching and syntactic parsing based systems. The systems have modest accuracy and recall and are very restricted with respect to the number of features they can target.…”

Section: Related Workmentioning

confidence: 99%

“…A small corpus consisting of descriptive grammars of the natural languages spoken in South Asia was reported in (Borin et al, 2018), and a set of documents from that corpus annotated with LingFN frames was reported in (Virk et al, 2019). Annotation of a descriptive grammars with LingFN frames involve identification of lexical units and selection of appropriate linguistic semantic frames and their frame elements.…”

Section: Datamentioning

confidence: 99%

Automatic Extraction of Typological Linguistic Features from Descriptive Grammars

Virk

Borin

Saxena

et al. 2017

Text, Speech, and Dialogue

Self Cite

View full text Add to dashboard Cite

Linguistic typology is an area of linguistics concerned with analysis of and comparison between natural languages of the world based on their certain linguistic features. For that purpose, historically, the area has relied on manual extraction of linguistic feature values from textural descriptions of languages. This makes it a laborious and time expensive task and is also bound by human brain capacity. In this study, we present a deep learning system for the task of automatic extraction of linguistic features from textual descriptions of natural languages. First, textual descriptions are manually annotated with special structures called semantic frames. Those annotations are learned by a recurrent neural network, which is then used to annotate un-annotated text. Finally, the annotations are converted to linguistic feature values using a separate rule based module. Word embeddings, learned from general purpose text, are used as a major source of knowledge by the recurrent neural network. We compare the proposed deep learning system to a previously reported machine learning based system for the same task, and the deep learning system wins in terms of F1 scores with a fair margin. Such a system is expected to be a useful contribution for the automatic curation of typological databases, which otherwise are manually developed.

show abstract

Glossing in the Linguistic Survey of India

Lahaussois

2021

View full text Add to dashboard Cite

Summary In this article, I explore glossing practices in the period surrounding the publication of the Linguistic Survey of India (LSI), the large-scale survey of languages spoken on the Indian subcontinent at the turn of the 20th century, under the stewardship of George Abraham Grierson (1851–1941). After a brief discussion of the reasons that the LSI constitutes a useful corpus for studying glossing practices, I provide a detailed examination of the glossing practices used in the text specimens which accompany language descriptions in the LSI. I then contrast these practices with glossing in materials produced both prior to and subsequent to the LSI, in order to place the glossing practices established by Grierson within a historical context, thereby contributing a description of one step in the history of glossing of descriptive linguistic materials.

show abstract

Language Technology for Digital Linguistics: Turning the Linguistic Survey of India into a Rich Source of Linguistic Information

Cited by 5 publications

References 5 publications

Computational historical linguistics and language diversity in South Asia

Computational historical linguistics and language diversity in South Asia

Automatic Extraction of Typological Linguistic Features from Descriptive Grammars

Glossing in the Linguistic Survey of India

Contact Info

Product

Resources

About