Tophrase

Tophrase is a term used in natural language processing and information retrieval to refer to a small set of phrases whose content best represents a document, document set, or discourse segment. The concept is closely related to keyword extraction and keyphrase extraction, and in practice “top phrases” may comprise single words or multiword expressions.

Candidates for tophrases are typically noun phrases, proper nouns, or frequently co-occurring expressions. Scores are assigned using methods such as term frequency–inverse document frequency (tf-idf), mutual information, or more sophisticated approaches like graph-based algorithms (TextRank) and rapid automatic keyword extraction (RAKE). Language models and word or sentence embeddings can also estimate semantic salience and coherence to improve selection.

Applications of top phrases span several tasks. They are used for indexing and search, to improve document retrieval and tagging; for summarization and topic modeling to identify representative content; for content tagging and categorization; and in various forms of search engine optimization and content recommendation.

Evaluation of top-phrase systems typically involves precision, recall, and F1 measures, as well as ranking metrics such as mean average precision, often against human-annotated reference lists. Challenges include domain dependence, language complexity and multilinguality, ambiguity in phrase boundaries, and sensitivity to boilerplate or noisy text.

Tophrase serves as a practical concept in text analysis, enabling compact representations of textual content and supporting downstream tasks that rely on concise, salient descriptors of documents. See also keyword extraction, keyphrase, TextRank, RAKE, and TF-IDF.