| Challenge: | Neural word embeddings have proven valuable in the development of medical applications, but for the Italian language, there are no publicly available corpora, embedds, or evaluation resources tailored to this domain. |
| Approach: | They propose to use a corpus of medical texts to generate neural word embeddings in Italian using Metathesaurus concept graphs. |
| Outcome: | The results show that the new embeddings correlate well with human judgments regarding similarity and relatedness of medical concepts. |
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| Challenge: | Off-the-shelf word embeddings tend to perform poorly on texts from specialized domains such as clinical reports. |
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Evaluation Dataset for Japanese Medical Text Simplification (2024.naacl-srw)
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| Challenge: | Contextual word embedding models do not take into account structured expert domain knowledge from a knowledge base. |
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Is Language Modeling Enough? Evaluating Effective Embedding Combinations (2020.lrec-1)
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Rudolf Schneider, Tom Oberhauser, Paul Grundmann, Felix Alexander Gers, Alexander Loeser, Steffen Staab
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