Charagram: Embedding Words and Sentences via Character n-grams
John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu · 2016
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. 1