Architecture of the Linguistic Platform Turklang

Main Article Content

Airat Rafizovich Gatiatullin

Abstract

The article describes the architecture of the TurkLang multifunctional linguistic platform, currently being developed at the Institute of Applied Semiotics of the Academy of Sciences of the Republic of Tatarstan. The system is based on a three-level knowledge base: linguistic universals, descriptions of units of specific languages, and empirical speech data. The knowledge base is implemented as a graph, where the primary storage unit is the morpheme – the minimal meaningful unit of language (akin to an atomic data element). This design choice is driven by the element-combinatorial nature of Turkic languages: a word form (the concrete realization of a word in text) is a linear chain consisting of a root and sequentially attached affixes – modifiers, each expressing a single grammatical category (case, number, tense, etc.) in a strictly fixed order. The paper examines levels of abstraction, types of relationships between entities, and mechanisms for integration with external sources (an electronic encyclopedia, lexicographic resources, and GIS data). Special attention is paid to the application of international standards for syntactic (Universal Dependencies), semantic (Universal Meaning Representation), and pragmatic (DiAML) annotation. A comparison with existing platforms (Sketch Engine, LingvoDoc, Apertium) is provided. To date, the platform's knowledge base covers more than 30 languages and dialects, contains over 237,000 root morphemes and about 40,000 compatibility rules, while the corpus component comprises over 180 million of word occurrences.

Article Details

How to Cite
Gatiatullin, A. R. “Architecture of the Linguistic Platform Turklang”. Russian Digital Libraries Journal, vol. 29, no. 6, Oct. 2026, pp. 2330-56, doi:10.26907/1562-5419-2026-29-6-2330-2356.

References

1. Guzev V.G. O nekotorykh ekzoticheskikh osobennostyakh tyurkskikh yazykov («tyurkskie chudesa») // Aktual'nye problemy mirovoy politiki. 2020. Vol. 10. P. 231–245. https://doi.org/10.21638/11701/26868318.16
2. Koskenniemi K. Two-Level Morphology: A General Computational Model for Word-Form Recognition and Production. Helsinki: University of Helsinki, 1983. 160 p.
3. Apresyan Y.D. Izbrannye trudy. Tom II. Integral'noe opisanie yazyka i sistemnaya leksikografiya. Moscow: Shkola “Yazyki russkoi kul’tury”, 1995. 769 p.
4. Krauwer S. The basic language resource kit (BLARK) as the first milestone for the language resources roadmap // Proc. International workshop on speech and computer SPECOM-2003, Moscow, Russia, Oct. 2003. P. 8–15.
5. Kilgarriff A., Rychlý P., Smrž P., Tugwell D. The Sketch Engine // Proceedings of the Eleventh EURALEX International Congress, Lorient, France, Jul. 2004. P. 105–116.
6. Normanskaya Y.V., Borisenko O.D., Beloborodov I.B., Avetisyan A.I. The Software System LingvoDoc and the Possibilities It Offers for Documentation and Analysis of Ob-Ugric Languages // Doklady Mathematics. 2022. Vol. 105, No. 3. P. 187–206. https://doi.org/10.31857/S2686954322030055
7. Khanna, T., Washington, J.N., Tyers, F.M et al. Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages // Machine Translation. 2021. Vol. 35. P. 475–502. https://doi.org/10.1007/s10590-021-09260-6
8. Bakay Ö., Ergelen Ö., Sarmış E., Yıldırım S., et al. Turkish WordNet KeNet // Proceedings of the 11th Global Wordnet Conference, Virtual, Jan. 2021. P. 166–174. https://doi.org/10.18653/v1/2021.gwc-1.19
9. Agostini A., Usmanov T., Khamdamov U., Abdurakhmonova N., et al. UZWORDNET: A Lexical- Semantic Database for the Uzbek Language // Proceedings of the 11th Global Wordnet Conference, Virtual, Jan. 2021. P. 8–19. https://doi.org/10.18653/v1/2021.gwc-1.2
10. Marsan B., Kara N., Ozcelik M., Arican B. N., et al. Building the Turkish FrameNet // Proceedings of the 11th Global Wordnet Conference, Virtual, Jan. 2021. P. 118–125. https://doi.org/10.18653/v1/2021.gwc-1.14
11. Rezhake M., Kuerban A. Design of the Uyghur FrameNet Desktop // Lecture Notes on Software Engineering. 2015. Vol. 3. No. 1. P. 53–56. https://doi.org/10.7763/LNSE.2015.V3.165
12. Navigli R., Ponzetto S.P. BabelNet: The Automatic Construction, Evaluation and Application of a Wide-Coverage Multilingual Semantic Network // Artificial Intelligence. 2012. Vol. 193. P. 217–250. https://doi.org/10.1016/j.artint.2012.07.001
13. Gangemi A., Alam M., Asprino L., Presutti V., et al. Framester: A Wide Coverage Linguistic Linked Data Hub. // Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science. 2016. Vol. 10024. P. 239–254. https://doi.org/10.1007/978-3-319-49004-5 16
14. Nivre J., de Marneffe M-C., Ginter F., Hajič J., et al. Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection // Proceedings of the Twelfth Language Resources and Evaluation Conference (LREC 2020), Marseille, France, May. 2020. P. 4034–4043.