تقرير
Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation
| العنوان: | Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation |
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| المؤلفون: | Hoelscher-Obermaier, Jason, Stevinson, Edward, Stauber, Valentin, Zhelev, Ivaylo, Botev, Victor, Wu, Ronin, Minton, Jeremy |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language |
| الوصف: | The most interesting words in scientific texts will often be novel or rare. This presents a challenge for scientific word embedding models to determine quality embedding vectors for useful terms that are infrequent or newly emerging. We demonstrate how \gls{lsi} can address this problem by imputing embeddings for domain-specific words from up-to-date knowledge graphs while otherwise preserving the original word embedding model. We use the MeSH knowledge graph to impute embedding vectors for biomedical terminology without retraining and evaluate the resulting embedding model on a domain-specific word-pair similarity task. We show that LSI can produce reliable embedding vectors for rare and OOV terms in the biomedical domain. Comment: Accepted for the Workshop on Information Extraction from Scientific Publications at AACL-IJCNLP 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2210.15358 |
| رقم الأكسشن: | edsarx.2210.15358 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |