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Charagram: Embedding Words and Sentences via Character n-grams

arXiv 1607.02789 ↗First posted 10 Jul 2016cs.CLcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jul 2016.

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Citing papers per year: 2021: 6; 2022: 3; 2023: 3; 2024: 1; 2025: 1.

14 papers.

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  1. 2021CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representationcited by 229
  2. 2021Dynamic Language Models for Continuously Evolving Contentcited by 27
  3. 2023Text Rendering Strategies for Pixel Language Modelscited by 22
  4. 2024Large Language Models Lack Understanding of Character Composition of Wordscited by 21
  5. 2021Paraphrastic Representations at Scalecited by 19
  6. 2022Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Costcited by 9
  7. 2021Evaluating Neural Word Embeddings for Sanskritcited by 8
  8. 2023SanskritShala: A Neural Sanskrit NLP Toolkit with Web-Based Interface for Pedagogical and Annotation Purposescited by 5
  9. 2021Integrating Approaches to Word Representationcited by 4
  10. 2021Explaining Natural Language Processing Classifiers with Occlusion and Language Modelingcited by 2
  11. 2023Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskritcited by 1
  12. 20221Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Taskcited by 0
2 more
  1. 2022Efficient Entity Candidate Generation for Low-Resource Languagescited by 0
  2. 2025Handling Korean Out-of-Vocabulary Words with Phoneme Representation Learningcited by 0