-
-
Autor*innen: Kim, Jungi; Nam, Jinseok; Gurevych, Iryna
Titel: Learning semantics with deep belief network for cross-language information retrieval
Aus: Kay, Martin; Boitet, Christian (Hrsg.): Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), Mumbai: The COLING 2012 Organizing Committee, 2012 , S. 579-588
URL: http://aclweb.org/anthology-new/C/C12/C12-2057.pdf
Dokumenttyp: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Computerlinguistik; Information Retrieval; Mehrsprachigkeit; Semantik
Abstract: This paper introduces a cross-language information retrieval (CLIR) framework that combines
the state-of-the-art keyword-based approach with a latent semantic-based retrieval model. To
capture and analyze the hidden semantics in cross-lingual settings, we construct latent semantic
models that map text in different languages into a shared semantic space. Our proposed
framework consists of deep belief networks (DBN) for each language and we employ canonical
correlation analysis (CCA) to construct a shared semantic space. We evaluated the proposed
CLIR approach on a standard ad hoc CLIR dataset, and we show that the cross-lingual semantic
analysis with DBN and CCA improves the state-of-the-art keyword-based CLIR performance.
DIPF-Abteilung: Informationszentrum Bildung