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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.20369 |
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| _version_ | 1866918035113115648 |
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| author | Bakni, Michel Diraneyya, Abbad Tellat, Wael |
| author_facet | Bakni, Michel Diraneyya, Abbad Tellat, Wael |
| contents | Term bases are recognized as one of the most effective components of translation software in time saving and consistency. In spite of the many recent advances in natural language processing (NLP) and large language models (LLMs), major translation platforms have yet to take advantage of these tools to improve their term bases and support scalable content for underrepresented languages, which often struggle with localizing technical terminology. Language academies in the Arab World, for example, have struggled since the 1940s to unify the way new scientific terms enter the Arabic language at scale. This abstract introduces an open source tool, WikiTermBase, with a systematic approach for building a lexicographical database with over 900K terms, which were collected and mapped from a multitude of sources on a semantic and morphological basis. The tool was successfully implemented on Arabic Wikipedia to standardize translated English and French terms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WikiTermBase: An AI-Augmented Term Base to Standardize Arabic Translation on Wikipedia Bakni, Michel Diraneyya, Abbad Tellat, Wael Information Retrieval Term bases are recognized as one of the most effective components of translation software in time saving and consistency. In spite of the many recent advances in natural language processing (NLP) and large language models (LLMs), major translation platforms have yet to take advantage of these tools to improve their term bases and support scalable content for underrepresented languages, which often struggle with localizing technical terminology. Language academies in the Arab World, for example, have struggled since the 1940s to unify the way new scientific terms enter the Arabic language at scale. This abstract introduces an open source tool, WikiTermBase, with a systematic approach for building a lexicographical database with over 900K terms, which were collected and mapped from a multitude of sources on a semantic and morphological basis. The tool was successfully implemented on Arabic Wikipedia to standardize translated English and French terms. |
| title | WikiTermBase: An AI-Augmented Term Base to Standardize Arabic Translation on Wikipedia |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2505.20369 |