arXiv:2606.24359v1 Announce Type: new Abstract: This paper proposed an algorithm for part-of-speech (POS) tagging senses of a bilingual dictionary. The algorithm is applied on the Al-Mawrid Arabic-English dictionary. The tagging task is accomplished by transferring the POS tags of the English translation equivalences (TEs) to the dictionary senses after dis-ambiguities process. The English POS tags of senses are acquired from the Princeton WordNet. POS tagging of bilingual dictionary senses is prerequisite to link a bilingual dictionary to WordNet and/or standardizing that dictionary into Word
The continuous development in natural language processing and the increasing availability of computational resources make it feasible to address complex linguistic tasks like bilingual dictionary sense tagging.
This research contributes to foundational AI capabilities, especially for less-resourced languages, by improving the structured organization and interoperability of linguistic data, which is crucial for advanced NLP applications.
The ability to automatically tag parts-of-speech for bilingual dictionary senses streamlines the process of linking bilingual dictionaries to comprehensive lexical databases like WordNet, enhancing cross-lingual understanding in AI.
- · NLP researchers
- · Developers of multilingual AI models
- · Users of translation technologies
- · Linguistic data providers
- · Manual lexicographers for bilingual dictionaries
Improved accuracy and efficiency in processing and understanding non-English texts by AI systems.
Facilitation of better machine translation, cross-lingual information retrieval, and development of AI for diverse linguistic communities.
Potential for increased digital inclusion for speakers of less-resourced languages, bridging linguistic divides in the global AI landscape.
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