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• W2949548130 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.
• W2949548130 created "2019-06-27" @default.
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• W2949548130 date "2016-07-10" @default.
• W2949548130 modified "2023-09-27" @default.
• W2949548130 title "Charagram: Embedding Words and Sentences via Character n-grams" @default.
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• W2949548130 hasPublicationYear "2016" @default.
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