LLODifying Linguistic Glosses

Chiarcos, Christian; Ionov, Maxim; Rind-Pawlowski, Monika; Fäth, Christian; Schreur, Jesse Wichers; Nevskaya, Irina

doi:10.1007/978-3-319-59888-8_7

Cited by 6 publications

(9 citation statements)

References 5 publications

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“…The development of NLP resources for underdocumented languages like Armenian has seen noteworthy progress in morphological and syntactic analysis, as highlighted by several key studies (Dolatian et al, 2022;Kindt and Kepeklian, 2022;Vidal-Gorène and Kindt, 2020;Ghukasyan and Avetisyan, 2021;Arkhangelskiy et al, 2012;Chiarcos et al, 2018).…”

Section: Progress In Morphology and Syntaxmentioning

confidence: 99%

Large Language Models and Low-Resource Languages: An Examination of Armenian NLP

Avetisyan,

Broneske

2023

Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

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This paper presents a comprehensive review of Natural Language Processing (NLP) research on Armenian, a language that, despite its rich history and unique linguistic characteristics, is currently low-resource in the field of NLP. We critically synthesize and evaluate various studies in Armenian NLP, highlighting key advancements, challenges, and areas for improvement. A notable aspect of our work is the underlined lack of application of Large Language Models (LLMs) in Armenian NLP, signifying an area of potential exploration and development. Identifying and discussing these challenges and opportunities lays the groundwork for future research directions in Armenian NLP. The emphasis on Armenian also advocates for increased attention to low-resource languages in NLP research, stressing the importance of linguistic diversity and equity. To the best of our knowledge, this is the first paper providing such an extensive review of Armenian NLP, marking a significant contribution to the field.

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Section: Progress In Morphology and Syntaxmentioning

confidence: 99%

Large Language Models and Low-Resource Languages: An Examination of Armenian NLP

Avetisyan,

Broneske

2023

Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)

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“…-MARL: a vocabulary for describing and annotating subjective opinions. 31 -ITSRDF: an ontology used within the Internationalization Tag Set. 32 -The Creative Commons vocabulary 33 (CC).…”

Section: Lld Models: An Overviewmentioning

confidence: 99%

“…The most well known model for the creation and publication of lexica and dictionaries as linked data is OntoLex-Lemon 45 [39] (see Appendix 7.1.2 for an introduction to the model with examples, and Section 5.1 for extensions and further developments). This was an output of the W3C Ontology-Lexica working group (we will refer to this as 31 45 The URI for OntoLex-Lemon is: http://www.w3.org/ns/lemon/ontolex and the OntoLex-Lemon guidelines can be found at https://www.w3.…”

Section: Lexica and Dictionariesmentioning

confidence: 99%

“…Other tools include interfaces for converting, validating, and exploring linguistic data to aid in linguistic research both within and outside of the project. Tool development and linguistic research are both integral parts of LiODi and the tools and pipelines implemented within it are also tested on the data generated and used in the project [21,31].…”

Section: Innovative Projects 621 Liodi (2015-2022)mentioning

confidence: 99%

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When linguistics meets web technologies. Recent advances in modelling linguistic linked data

Khan

Chiarcos

Declerck

et al. 2022

Self Cite

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This article provides a comprehensive and up-to-date survey of models and vocabularies for creating linguistic linked data (LLD) focusing on the latest developments in the area and both building upon and complementing previous works covering similar territory. The article begins with an overview of some recent trends which have had a significant impact on linked data models and vocabularies. Next, we give a general overview of existing vocabularies and models for different categories of LLD resource. After which we look at some of the latest developments in community standards and initiatives including descriptions of recent work on the OntoLex-Lemon model, a survey of recent initiatives in linguistic annotation and LLD, and a discussion of the LLD metadata vocabularies META-SHARE and lime. In the next part of the paper, we focus on the influence of projects on LLD models and vocabularies, starting with a general survey of relevant projects, before dedicating individual sections to a number of recent projects and their impact on LLD vocabularies and models. Finally, in the conclusion, we look ahead at some future challenges for LLD models and vocabularies. The appendix to the paper consists of a brief introduction to the OntoLex-Lemon model.

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“…This is the scope of several chapters in this volume. Likewise, LLOD-native applications are possible-for instance, to use RDFa (RDF in attributes; Herman et al 2015) to complement an XML workflow with SPARQL-based semantic search by means of web services (Tittel et al 2018); to provide aggregation, enrichment, and search routines for language resource metadata (Chiarcos et al 2016;McCrae and Cimiano 2015); to use RDF as a formalism for annotation integration and data management (Burchardt et al 2008;Pareja-Lora 2012;Chiarcos et al 2017); or to use RDF and SPARQL for manipulating and evaluating linguistic annotations (Chiarcos, Khait et al 2018;Chiarcos, Kosmehl et al 2018). While these applications demonstrate the potential of LOD technology in linguistics, they come with a considerable entry barrier, and they address the advanced user of RDF technology rather than a typical linguist.…”

Section: Chances Challenges and Prospectsmentioning

confidence: 99%

Open Data—Linked Data—Linked Open Data—Linguistic Linked Open Data (LLOD): A General Introduction

Chiarcos¹,

Pareja-Lora²

2020

Development of Linguistic Linked Open Data Resources for Collaborative Data-Intensive Research in the Language Sciences

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Background: Scientific Principles and OpennessIn recent decades, the scientific community has become increasingly aware of the importance of openness-for software (open source), publications (open access), structured data (open knowledge), and data collections in general (Open Data). Here, we focus on the latter aspect. Indeed, publishing data collections under open resources has become routine in modern-day research. In this initial chapter, we elaborate on motivations and conventions for publishing Open Data in linguistics and related areas.The Open Data movement in linguistics-as well as in all areas of study in science, computation, and humanities-draws on three main motivations: (1) responsibility, (2) reproducibility, and (3) reusability.

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LLODifying Linguistic Glosses

Cited by 6 publications

References 5 publications

Large Language Models and Low-Resource Languages: An Examination of Armenian NLP

Large Language Models and Low-Resource Languages: An Examination of Armenian NLP

When linguistics meets web technologies. Recent advances in modelling linguistic linked data

Open Data—Linked Data—Linked Open Data—Linguistic Linked Open Data (LLOD): A General Introduction

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