Bridging FLEx and Computational Linguistics: A Web-Based System for Bidirectional Conversion and FAIR-Compliant Interoperability
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105032727431
Journal Title
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025
Rights Holder(s)
SCOPUS
Bibliographic Citation
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025)
Suggested Citation
Limpisiri T., Leenoi D., Panutatpinyo R., Chongkolrattanapond P. Bridging FLEx and Computational Linguistics: A Web-Based System for Bidirectional Conversion and FAIR-Compliant Interoperability. 2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025). doi:10.1109/iSAI-NLP66160.2025.11320696 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115811
Title
Bridging FLEx and Computational Linguistics: A Web-Based System for Bidirectional Conversion and FAIR-Compliant Interoperability
Corresponding Author(s)
Other Contributor(s)
Abstract
FieldWorks Language Explorer (FLEx) is widely used for documenting under-resourced languages, but its XML based formats (SFM, LIFT) hinder integration with computational workflows that rely on tabular data. This paper introduces a web-based system for near-lossless, bidirectional conversion between FLEx formats and CSV/Excel. The tool provides configurable field mapping, validation diagnostics, and support for media references, enabling linguists to edit data in familiar tabular form and reimport it into FLEx with minimal technical overhead. Evaluation across five Thai lexical resources (110k+ entries) demonstrates consistent structural fidelity, efficient conversion, and successful preservation of media references. A user study with ten participants highlights strong usability and reliability, though challenges remain in handling complex LIFT structures, restricted audio integration, and large-scale corpora. By lowering barriers for non-specialist users and aligning with FAIR data principles, this work establishes a practical pathway for integrating FLEx resources into broader NLP pipelines.