Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining
| Autor: | Kata Gábor, Haïfa Zargayouna, Thierry Charnois, Isabelle Tellier, Davide Buscaldi |
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| Přispěvatelé: | Laboratoire d'Informatique de Paris-Nord (LIPN), Université Sorbonne Paris Cité (USPC)-Institut Galilée-Université Paris 13 (UP13)-Centre National de la Recherche Scientifique (CNRS), Lattice - Langues, Textes, Traitements informatiques, Cognition - UMR 8094 (Lattice), Université Sorbonne Nouvelle - Paris 3-Université Sorbonne Paris Cité (USPC)-Centre National de la Recherche Scientifique (CNRS)-Université Paris sciences et lettres (PSL)-Département Littératures et langage (LILA), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL), Département Littératures et langage - ENS Paris (LILA), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Sorbonne Paris Cité (USPC)-Université Sorbonne Nouvelle - Paris 3 |
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Parse tree 02 engineering and technology computer.software_genre Relationship extraction 020204 information systems 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] 020201 artificial intelligence & image processing Data mining Artificial intelligence Sequential Pattern Mining Unsupervised clustering business computer Natural language processing Word (computer architecture) Semantic relation |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319463483 IDA XVIth Symposium on Intelligent Data Analysis XVIth Symposium on Intelligent Data Analysis, Oct 2016, Stockholm, Sweden. pp.237-248, ⟨10.1007/978-3-319-46349-0_21⟩ |
| DOI: | 10.1007/978-3-319-46349-0_21 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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