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• W2463895987 abstract "We present Charagram embeddings, a simple approach for learning character-based compositional models to embed textual sequences. A word or sentence is represented using a character n-gram count vector, followed by a single nonlinear transformation to yield a low-dimensional embedding. We use three tasks for evaluation: word similarity, sentence similarity, and part-of-speech tagging. We demonstrate that Charagram embeddings outperform more complex architectures based on character-level recurrent and convolutional neural networks, achieving new state-of-the-art performance on several similarity tasks." @default.
• W2463895987 created "2016-07-22" @default.
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• W2463895987 date "2016-01-01" @default.
• W2463895987 modified "2023-10-01" @default.
• W2463895987 title "Charagram: Embedding Words and Sentences via Character n-grams" @default.
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• W2463895987 doi "https://doi.org/10.18653/v1/d16-1157" @default.
• W2463895987 hasPublicationYear "2016" @default.
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