Matuschek, Michael ; Miller, Tristan ; Gurevych, Iryna
eds.: Ruppenhofer, Josef ; Faaß, Gertrud (2014)
A Language-independent Sense Clustering Approach for Enhanced WSD.
Hildesheim, Germany
Conference or Workshop Item
Abstract
We present a method for clustering word senses of a lexical-semantic resource by mapping them to those of another sense inventory. This is a promising way of reducing polysemy in sense inventories and consequently improving word sense disambiguation performance. In contrast to previous approaches, we use Dijkstra-WSA, a parameterizable alignment algorithm which is largely resource- and language-agnostic. To demonstrate this, we apply our technique to GermaNet, the German equivalent to WordNet. The GermaNet sense clusterings we induce through alignments to various collaboratively constructed resources achieve a significant boost in accuracy, even though our method is far less complex and less dependent on language-specific knowledge than past approaches.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2014 |
| Editors: | Ruppenhofer, Josef ; Faaß, Gertrud |
| Creators: | Matuschek, Michael ; Miller, Tristan ; Gurevych, Iryna |
| Type of entry: | Bibliographie |
| Title: | A Language-independent Sense Clustering Approach for Enhanced WSD |
| Language: | English |
| Date: | October 2014 |
| Publisher: | Universitätsverlag Hildesheim |
| Book Title: | Proceedings of the 12th Konferenz zur Verarbeitung natürlicher Sprache (KONVENS 2014) |
| Event Location: | Hildesheim, Germany |
| URL / URN: | https://fileserver.ukp.informatik.tu-darmstadt.de/UKP_Webpag... |
| Corresponding Links: | |
| Abstract: | We present a method for clustering word senses of a lexical-semantic resource by mapping them to those of another sense inventory. This is a promising way of reducing polysemy in sense inventories and consequently improving word sense disambiguation performance. In contrast to previous approaches, we use Dijkstra-WSA, a parameterizable alignment algorithm which is largely resource- and language-agnostic. To demonstrate this, we apply our technique to GermaNet, the German equivalent to WordNet. The GermaNet sense clusterings we induce through alignments to various collaboratively constructed resources achieve a significant boost in accuracy, even though our method is far less complex and less dependent on language-specific knowledge than past approaches. |
| Uncontrolled Keywords: | reviewed;UKP_a_LangTech4eHum |
| Identification Number: | TUD-CS-2014-0878 |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Ubiquitous Knowledge Processing |
| Date Deposited: | 31 Dec 2016 14:29 |
| Last Modified: | 24 Jan 2020 12:03 |
| PPN: | |
| Corresponding Links: | |
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