Linked Data for Language-Learning Applications

Loughnane, Robyn; McCurdy, Kate; Kolb, Peter; Selent, Stefan

doi:10.18653/v1/w17-5005

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…Bulgarian, Dutch, English, Finnish, French, etc.) [213], Linking Latin project (LiLa) [195] in Latin, Framester data [99], OLiA annotation and linking models [54] (more than 75 language varieties) Syntax and morphology Ontolex-Synset module, OntoLex-Morph module, Multilingual Morpheme Ontology (MMoOn) [138,139] OLiA annotation and linking models [54] (more than 75 language varieties), Resource from Loughnane et al [155] in English and Spanish, Linking Latin project (LiLa) [195] in Latin, OdeNet in RDF in German [77] Pragmatics Pareja-Lora [194], OLiA Discourse Extensions [39], SemDok [8] Discourse marker annotations in Bulgarian, Lithuanian, German, European Portuguese, Hebrew, Romanian, Polish, and Macedonian, and English [215], Resource from [46] in 15 language varietes (e.g. English, German, Spanish, Arabic, etc) Lexicography Ontolex-Lexicog Apertium 22 bilingual lexicons in RDF [117] (eg.…”

Section: Resultsmentioning

confidence: 99%

“…singular and plural forms. A very specific scenario for re-using OntoLex-Lemon to model morphological and syntactic information is provided by Loughnane et al [155], who target to represent annotations generated from language-learning content. As examples, the authors model a Spanish conjugation and an English syntax exercise as LLD.…”

Section: Syntax and Morphologymentioning

confidence: 99%

“…A detailed overview of how (multilingual) knowledge graphs have been relevant for and used in NLP tasks is provided by Schneider et al [212], ranging from entity alignment to text summarization. LLOD resources have also been beneficial to many application domains, such as cultural heritage [35,105], healthcare and medicine [111], administration and law [83], e-governance [159,221], media and journalism [219], language learning and education [155], crosscultural business and commerce [90,229], disaster response and humanitarian aid [34], ecology and environment [4], and digital librarianship [69].…”

Section: Resources and Their Usementioning

confidence: 99%

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Multilinguality and LLOD: A survey across linguistic description levels

Gromann,

Apostol,

Chiarcos

et al. 2024

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Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

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Section: Resultsmentioning

confidence: 99%

Section: Syntax and Morphologymentioning

confidence: 99%

Section: Resources and Their Usementioning

confidence: 99%

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Multilinguality and LLOD: A survey across linguistic description levels

Gromann,

Apostol,

Chiarcos

et al. 2024

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Data mining in foreign language learning

Bravo‐Agapito

Bonilla

Seoane

2018

WIREs Data Min & Knowl

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Educational data mining (EDM) combines the techniques of data mining with educational data in order to provide students, instructors, and researchers with knowledge that can benefit academic processes. Due to globalization, foreign language learning (FLL) has become increasingly important. This work seeks to gain insight as to how data mining (DM) is being used to benefit FLL. For this purpose, an advanced review of pertinent research published from 2012 to 2017 was performed. After applying our screening method, 208 papers were selected for the exhaustive analysis. This analysis was divided into four aspects: context (educational environments, educational level), number of items, DM methods, and DM applications. The results indicated that 54% of studies were conducted in traditional environments, while only 3% of studies were performed in an m‐learning environment. In addition, 25 and 72% of the research was conducted in either a primary or secondary level, or in tertiary or adult level, respectively. Likewise, 76% of studies contained datasets of less than 1,000 items. The most utilized EDM methods were: factor analysis, regression, text mining, correlation mining, and causal DM. In addition, the studies analyzed showed that DM is mainly employed to predict the performance of students, to check learners' motivation, and to provide feedback for instructors. These results seem to indicate that although DM has much to offer the increasing number of language students, it is not being used to its full potential. This article is categorized under: Application Areas > Education and Learning Fundamental Concepts of Data and Knowledge > Data Concepts

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Linked Data for Language-Learning Applications

Cited by 2 publications

References 22 publications

Multilinguality and LLOD: A survey across linguistic description levels

Multilinguality and LLOD: A survey across linguistic description levels

Data mining in foreign language learning

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