Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining

Kata Gábor, Haïfa Zargayouna, Isabelle Tellier, Davide Buscaldi, Thierry Charnois

Abstract: This paper deals with the extraction of semantic relations from scientific texts. Pattern-based representations are compared to word embeddings in unsupervised clustering experiments, according to their potential to discover new types of semantic relations and recognize their instances. The results indicate that sequential pattern mining can significantly improve pattern-based representations, even in a completely unsupervised setting.