TextEmbeddings

TextEmbeddings refer to dense vector representations of text designed to encode semantic and syntactic information in a numeric form. They map text units—word, sentence, or document—into real-valued vectors such that linguistic similarity is reflected by vector proximity. Word embeddings capture word-level semantics, while sentence and document embeddings aim to represent larger text spans.

Common methods include static embeddings like Word2Vec, GloVe, and fastText, which learn a fixed vector per word from co-occurrence statistics or prediction tasks. Contextual embeddings produced by transformer models (BERT, RoBERTa, GPT) generate vectors that depend on surrounding context and can be used at token, sentence, or document granularity. Sentence or paragraph embeddings include models such as Sentence-BERT and Universal Sentence Encoder. Training objectives range from predicting neighboring words to masked language modeling, next-sentence prediction, or contrastive objectives for sentence representations.

Applications include semantic search and information retrieval, clustering and topic modeling, paraphrase detection, text classification, and multimodal tasks when combined with other modalities. They are often used with vector databases and nearest-neighbor search methods (e.g., FAISS, ScaNN) for scalable similarity search. Evaluation can be intrinsic (word similarity, analogy tasks) or extrinsic (performance on downstream tasks).

Limitations include bias and fairness concerns, domain shift, fixed dimensionality in static embeddings, and resource requirements for training or fine-tuning large models. Pre-trained embeddings are widely available across languages and domains, with fine-tuning or feature extraction enabling many practical applications.