Character Embedding
Character embedding is a natural language processing (NLP) technique that represents individual characters (e.g., letters, digits, punctuation) as dense, low-dimensional vectors in a continuous vector space. It captures morphological, phonetic, and semantic information at the character level, enabling models to handle out-of-vocabulary words, misspellings, and morphologically rich languages. This approach is particularly useful for tasks like text classification, named entity recognition, and machine translation where word-level embeddings may be insufficient.
Developers should learn character embedding when working on NLP projects involving languages with complex morphology (e.g., Turkish, Finnish), handling noisy text data with typos or slang, or dealing with domain-specific terminology not covered by pre-trained word embeddings. It is essential for building robust models in applications like social media analysis, biomedical text processing, or low-resource language tasks where word-based methods fail due to data sparsity.