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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2604.14397 |
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| _version_ | 1866908967777599488 |
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| author | Basil, David Girigowda, Chirooth Hauer, Bradley Momin, Sahir Shi, Ning Kondrak, Grzegorz |
| author_facet | Basil, David Girigowda, Chirooth Hauer, Bradley Momin, Sahir Shi, Ning Kondrak, Grzegorz |
| contents | We study the task of automatically expanding WordNet-style lexical resources to new languages through sense generation. We generate senses by associating target-language lemmas with existing lexical concepts via semantic projection. Given a sense-tagged English corpus and its translation, our method projects English synsets onto aligned target-language tokens and assigns the corresponding lemmas to those synsets. To generate these alignments and ensure their quality, we augment a pre-trained base aligner with a bilingual dictionary, which is also used to filter out incorrect sense projections. We evaluate the method on multiple languages, comparing it to prior methods, as well as dictionary-based and large language model baselines. Results show that the proposed project-and-filter strategy improves precision while remaining interpretable and requiring few external resources. We plan to make our code, documentation, and generated sense inventories accessible. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_14397 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generating Concept Lexicalizations via Dictionary-Based Cross-Lingual Sense Projection Basil, David Girigowda, Chirooth Hauer, Bradley Momin, Sahir Shi, Ning Kondrak, Grzegorz Computation and Language Artificial Intelligence We study the task of automatically expanding WordNet-style lexical resources to new languages through sense generation. We generate senses by associating target-language lemmas with existing lexical concepts via semantic projection. Given a sense-tagged English corpus and its translation, our method projects English synsets onto aligned target-language tokens and assigns the corresponding lemmas to those synsets. To generate these alignments and ensure their quality, we augment a pre-trained base aligner with a bilingual dictionary, which is also used to filter out incorrect sense projections. We evaluate the method on multiple languages, comparing it to prior methods, as well as dictionary-based and large language model baselines. Results show that the proposed project-and-filter strategy improves precision while remaining interpretable and requiring few external resources. We plan to make our code, documentation, and generated sense inventories accessible. |
| title | Generating Concept Lexicalizations via Dictionary-Based Cross-Lingual Sense Projection |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.14397 |