Embeddings are numerical representations of text that capture semantic meaning, making them valuable for document classification. Unlike traditional methods (like keyword counts or TF-IDF), embeddings convert words, sentences, or entire documents into dense vectors in a way that similar content clusters closer in vector space. This allows machine learning models to process text as structured numerical data, which is essential for tasks like categorizing emails as spam, labeling news articles by topic, or sorting customer feedback into sentiment categories. By translating text into vectors, embeddings enable algorithms to detect patterns based on meaning rather than just surface-level keywords.
One key application is improving feature representation for classification models. For example, embeddings can handle synonyms and related terms effectively: the words "film" and "movie" might map to similar vectors, so a classifier trained on embeddings will treat them as related even if they don’t appear in the same training data. This reduces the need for manual feature engineering. Embeddings also handle variable-length documents efficiently. Techniques like averaging word embeddings (e.g., Word2Vec) or using document-specific embeddings (e.g., Doc2Vec) convert entire texts into fixed-length vectors, which simplifies input for models like neural networks. For instance, a support ticket classification system could use averaged embeddings to represent each ticket as a 300-dimensional vector, regardless of its length, and feed it into a logistic regression or neural network classifier.
Another major use case is leveraging pre-trained embeddings for transfer learning. Models like BERT or Universal Sentence Encoder provide embeddings trained on vast text corpora, capturing general language patterns. Developers can fine-tune these embeddings on smaller domain-specific datasets (e.g., medical records or legal documents) to improve classification accuracy without needing massive labeled data. For example, a developer building a legal document classifier could start with pre-trained BERT embeddings, then fine-tune them on a dataset of contracts labeled by type (e.g., "NDA," "employment agreement"). This approach often outperforms training embeddings from scratch, especially when labeled data is limited. Additionally, embeddings enable hybrid approaches, such as combining them with metadata (e.g., document source or creation date) to enhance model performance.