Word embeddings as autonomous predictors in materials design—the effect of inherent variability on information transfer [PDF]
Journal of CheminformaticsWe propose that word embeddings of atoms derived from scientific literature are revisited as autonomous machine learning predictors in materials design.
Jana Radaković +2 more
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Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish [PDF]
Frontiers in Artificial Intelligence, 2022 Resources for Natural Language Processing (NLP) are less numerous for languages different from English. In the clinical domain, where these resources are vital for obtaining new knowledge about human health and diseases, creating new resources for the ...
Carolina Chiu +10 more
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Using Word Embeddings to Investigate Cultural Biases [PDF]
British Journal of Social Psychology, 2023 Kevin Durrheim
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Comparing general and specialized word embeddings for biomedical named entity recognition [PDF]
PeerJ Computer Science, 2021 Increased interest in the use of word embeddings, such as word representation, for biomedical named entity recognition (BioNER) has highlighted the need for evaluations that aid in selecting the best word embedding to be used.
Rigo E. Ramos-Vargas +2 more
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Historical Representations of Social Groups Across 200 Years of Word Embeddings From Google Books [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2022 Tessa E S Charlesworth +2 more
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Improved Biomedical Word Embeddings in the Transformer Era [PDF]
Journal of Biomedical Informatics, 2021 Jiho Noh, Ramakanth Kavuluru
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Clustering and Visualising Documents using Word Embeddings
The Programming Historian, 2023 This lesson uses word embeddings and clustering algorithms in Python to identify groups of similar documents in a corpus of approximately 9,000 academic abstracts.
Jonathan Reades, Jennie Williams
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Benefiting from Structured Resources to Present a Computationally Efficient Word Embedding Method [PDF]
Journal of Artificial Intelligence and Data Mining, 2022 In recent years, new word embedding methods have clearly improved the accuracy of NLP tasks. A review of the progress of these methods shows that the complexity of these models and the number of their training parameters grows increasingly.
F. Jafarinejad
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Improving Word Embedding Using Variational Dropout
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Pre-trained word embeddings are essential in natural language processing (NLP). In recent years, many post-processing algorithms have been proposed to improve the pre-trained word embeddings.
Zainab Albujasim +3 more
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Neuro-Symbolic Word Embedding Using Textual and Knowledge Graph Information
Applied Sciences, 2022 The construction of high-quality word embeddings is essential in natural language processing. In existing approaches using a large text corpus, the word embeddings learn only sequential patterns in the context; thus, accurate learning of the syntax and ...
Dongsuk Oh, Jungwoo Lim, Heuiseok Lim
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