Szarvas, György ; Zesch, Torsten ; Gurevych, Iryna
Hrsg.: Gelbukh, Alexander (2011)
Combining heterogeneous knowledge resources for improved distributional semantic models.
In: Proceedings of the 12th International Conference on Intelligent Text Processing and Computational Linguistics
Buchkapitel, Bibliographie
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Buchkapitel |
|---|---|
| Erschienen: | 2011 |
| Herausgeber: | Gelbukh, Alexander |
| Autor(en): | Szarvas, György ; Zesch, Torsten ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Combining heterogeneous knowledge resources for improved distributional semantic models |
| Sprache: | Englisch |
| Publikationsjahr: | 2011 |
| Verlag: | Springer |
| Buchtitel: | Proceedings of the 12th International Conference on Intelligent Text Processing and Computational Linguistics |
| Reihe: | Lecture Notes in Computer Science |
| Band einer Reihe: | 6608 |
| URL / URN: | https://www.springer.com/gp/book/9783642193996 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | UKP_p_ASC;UKP_p_SIGMUND |
| ID-Nummer: | TUD-CS-2011-0069 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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