Unsupervised Topic-Oriented Keyphrase Extraction and its Application to Croatian (CROSBI ID 173792)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Saratlija, Josip ; Šnajder, Jan ; Dalbelo Bašić, Bojana
engleski
Unsupervised Topic-Oriented Keyphrase Extraction and its Application to Croatian
Labeling documents with keyphrases is a tedious and expensive task. Most approaches to automatic keyphrases extraction rely on supervised learning and require manually labeled training data. In this paper we propose a fully unsupervised keyphrase extraction method, differing from the usual generic keyphrase extractor in the manner the keyphrases are formed. Our method begins by building topically related word clusters from which document keywords are selected, and then expands the selected keywords into syntactically valid keyphrases. We evaluate our approach on a Croatian document collection annotated by eight human experts, taking into account the high subjectivity of the keyphrase extraction task. The performance of the proposed method reaches up to F1=44.5, which is outperformed by human annotators, but comparable to a supervised approach.
Information extraction; keyphrase extraction; unsupervised learning; k-means; Croatian language
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano