2011 | OriginalPaper | Buchkapitel
Combining Heterogeneous Knowledge Resources for Improved Distributional Semantic Models
verfasst von : György Szarvas, Torsten Zesch, Iryna Gurevych
Erschienen in: Computational Linguistics and Intelligent Text Processing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The Explicit Semantic Analysis (ESA) model based on term cooccurrences in Wikipedia has been regarded as state-of-the-art semantic relatedness measure in the recent years. We provide an analysis of the important parameters of ESA using datasets in five different languages. Additionally, we propose the use of ESA with multiple lexical semantic resources thus exploiting multiple evidence of term cooccurrence to improve over the Wikipedia-based measure. Exploiting the improved robustness and coverage of the proposed combination, we report improved performance over single resources in word semantic relatedness, solving word choice problems, classification of semantic relations between nominals, and text similarity.