: Vocabulario de PLN: one-hot encoding

NLP methods and tools > one-hot encoding

one-hot encoding

Here we use one-hot encoding to transform the relations into integer vectors. (Chernyavskiy, Ilvovsky & Nakov, 2024)
Note that DIET can incorporate pre-trained word and sentence embeddings from language models as dense features with the flexibility to combine these with token level one-hot encodings and multi-hot encodings of character n-grams as sparse features. (Okur, Sahay & Nachman, 2022)
One-hot encoding is employed in classification tasks assigning values from 1 to 4 as labels to train the classifiers where each classifier is tailored to a different retrieval noise type. (Fang, Bai, Ni, Yang, Chen & Xu, 2024)
We embed POStags as vectors using one-hot encoding. (Chernyavskiy & Ilvovsky, 2020)

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