Results 21 to 30 of about 19,032 (297)
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
exaly +2 more sources
Improved biomedical word embeddings in the transformer era [PDF]
Journal of Biomedical Informatics, 2021 Jiho Noh, Ramakanth Kavuluru
exaly +2 more sources
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 2020 Word embeddings are a widely used set of natural language processing techniques that map words to vectors of real numbers. These vectors are used to improve the quality of generative and predictive models. Recent studies demonstrate that word embeddings contain and amplify biases present in data, such as stereotypes and prejudice.
Orestis Papakyriakopoulos +3 more
openaire +1 more source
Deterministic Compression of Word Embeddings
IEEE AccessWord embeddings are an indispensable technology in the field of artificial intelligence, particularly when working with natural language processing models.
Yuki Nakamura +3 more
doaj +2 more sources
Attention Word Embedding [PDF]
Proceedings of the 28th International Conference on Computational Linguistics, 2020 Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) model of word2vec learns a vector embedding by masking a given word in a sentence and then using the other words as a context to predict it.
Shashank Sonkar +2 more
openaire +2 more sources
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
doaj +1 more source
Word Embeddings as Statistical Estimators. [PDF]
Sankhya Ser BWord embeddings are a fundamental tool in natural language processing. Currently, word embedding methods are evaluated on the basis of empirical performance on benchmark data sets, and there is a lack of rigorous understanding of their theoretical properties.
Dey N +3 more
europepmc +5 more sources
Morphological Word-Embeddings [PDF]
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2015 Published at NAACL ...
Ryan Cotterell, Hinrich Schütze
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Domain-Specific Word Embeddings with Structure Prediction [PDF]
, 2023 Complementary to finding good general word embeddings, an important question for representation learning is to find dynamic word embeddings, for example, across time or domain. Current methods do not offer a way to use or predict information on structure
David Lassner +7 more
core +1 more source
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
doaj +1 more source