Single Document Keyphrase Extraction Using Sentence Clustering and Latent Dirichlet Allocation

Claude Pasquier (1)

1 IBV - Institut de Biologie Valrose

This paper describes the design of a system for extracting keyphrases from a single document The principle of the algorithm is to cluster sentences of the documents in order to highlight parts of text that are semantically related. The clusters of sentences, that reflect the themes of the document, are then analyzed to find the main topics of the text. Finally, the most important words, or groups of words, from these topics are proposed as keyphrases.

Mots clés: Natural language processing; Artificial intellience

Domaines: Intelligence artificielle [cs.AI]; Informatique et langage [cs.CL]

Claude Pasquier. Single Document Keyphrase Extraction Using Sentence Clustering and Latent Dirichlet Allocation. 5th International Workshop on Semantic Evaluation (SemEval '10), Jul 2010, Uppsala, Sweden. pp.154-157. ⟨hal-01151516⟩