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- W2891680269 abstract "We approach the problem of generalizing pre-trained word embeddings beyond fixed-size vocabularies without using additional contextual information. We propose a subword-level word vector generation model that views words as bags of character n-grams. The model is simple, fast to train and provides good vectors for rare or unseen words. Experiments show that our model achieves state-of-the-art performances in English word similarity task and in joint prediction of part-of-speech tag and morphosyntactic attributes in 23 languages, suggesting our model’s ability in capturing the relationship between words’ textual representations and their embeddings." @default.
- W2891680269 created "2018-09-27" @default.
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- W2891680269 date "2018-01-01" @default.
- W2891680269 modified "2023-10-16" @default.
- W2891680269 title "Generalizing Word Embeddings using Bag of Subwords" @default.
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- W2891680269 doi "https://doi.org/10.18653/v1/d18-1059" @default.
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