Named Entity Discovery and Alignment in Parallel Data.
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025) |
| MU Faculty or unit | |
| Citation | |
| web | https://www.insticc.org/node/TechnicalProgram/ICAART/2025/presentationDetails/133113 |
| Keywords | Named Entity Recognition; Named Entity Alignment; Named Entity Discovery; Named Entity Linking |
| Description | The paper describes two experiments with named entity discovery and alignment for English-Czech parallel data. In the previous work, we enriched the Parallel Global Voices corpus with named entity recognition (NER) for both languages and named entity linking (NEL) annotations for English. The alignment experiment employs sentence transformers and cosine similarity to identify NE translations from English to Czech and possibly other languages. The discovery experiment uses the same method to find possible translations between named entities in English and Czech n-grams. The described method achieves an F1 score of 0.94 in finding alignments between recognized entities. However, the same method can also discover unknown named entities with an F1 score of 0.70. The result indicates the method can be used to recognize named entities in parallel data in cases where no NER model is available with sufficient quality. |
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