Phrasei

Phrasei is a term used in theoretical linguistics and natural language processing to denote a compact representation of a linguistic phrase that can be used by algorithms. It functions as a unit that captures both structural and semantic information about a phrase, enabling operations at the phrase level rather than solely at the word level.

A phrasei object can be realized in two common forms. In symbolic form, it typically includes a span (start and end indices in the text), a syntactic category (such as NP, VP, or PP), a head word, morphological features, and optional attributes like its function within a larger clause, a semantic role, or dependency relations. In continuous form, a phrasei may be an embedding in a vector space learned by a model, where the vector encodes aspects of the phrase’s syntax and meaning.

Phrasei can be extracted from parse trees, chunking outputs, or produced by end-to-end models that generate phrase-level representations. It is used to support tasks that operate on phrases as units, such as phrase-based alignment, substitution, expansion, or aggregation in downstream NLP applications like parsing, machine translation, information extraction, question answering, and summarization.

Related concepts include phrase chunks, constituents, phrase embeddings, and constituency parsing. Phrasei is primarily a design abstraction for studying and engineering systems that manipulate phrases as first-class objects, offering a flexible framework for combining structural and semantic information at the phrase level.