Sentencee

Sentencee is a term used in linguistics and natural language processing to denote a sentence-level representation, a compact numerical encoding that summarizes a sentence's semantic content and syntactic structure. In practice, a sentencee is produced by an encoder—such as a recurrent neural network, a transformer, or a feature-based model—and is typically a fixed-length vector suitable for arithmetic operations and similarity comparisons.

Construction and variation: Sentencee vectors are typically created by pooling token-level representations produced by the encoder. Common strategies include mean pooling, max pooling, attention-based pooling, or using a special token (such as a CLS vector) in transformer models. Different models and training objectives (supervised tasks, unsupervised objectives like next-sentence prediction) influence the properties of the resulting sentencee.

Applications: Sentencee representations enable tasks such as semantic textual similarity, paraphrase detection, clustering, information retrieval, and downstream classification. They are used in search engines, chatbots, and text analysis pipelines to compare sentences or retrieve relevant passages.

Relation to sentence embeddings: The term sentencee is closely related to the more established notion of sentence embeddings. In many settings, sentencee and sentence embedding are used interchangeably, though some authors treat sentencee as a branded term or a specific implementation.

Status and usage: The term sentencee is not universally standardized and may appear variably in literature and industry. It is primarily described here as a conceptual label for sentence-level representations rather than a distinct, universally defined theory.

See also: sentence embedding, natural language processing, word embedding, transformer.