Gurevych, Iryna (2005)
Using the structure of a conceptual network in computing semantic relatedness.
Second International Joint Conference on Natural Language Processing. Jeju Island, Korea (11.10.2005-13.10.2005)
doi: 10.1007/11562214_67
Conference or Workshop Item, Bibliographie
Abstract
We present a new method for computing semantic relatedness of concepts. The method relies solely on the structure of a conceptual network and eliminates the need for performing additional corpus analysis. The network structure is employed to generate artificial conceptual glosses. They replace textual definitions proper written by humans and are processed by a dictionary based metric of semantic relatedness [1]. We implemented the metric on the basis of GermaNet, the German counterpart of WordNet, and evaluated the results on a German dataset of 57 word pairs rated by human subjects for their semantic relatedness. Our approach can be easily applied to compute semantic relatedness based on alternative conceptual networks, e.g. in the domain of life sciences.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2005 |
| Creators: | Gurevych, Iryna |
| Type of entry: | Bibliographie |
| Title: | Using the structure of a conceptual network in computing semantic relatedness |
| Language: | English |
| Date: | 2005 |
| Place of Publication: | Berlin |
| Publisher: | Springer Verlag |
| Book Title: | Proceedings of the Second International Joint Conference on Natural Language Processing |
| Series: | Lecture Notes in Artificial Intelligence |
| Series Volume: | 3651 |
| Event Title: | Second International Joint Conference on Natural Language Processing |
| Event Location: | Jeju Island, Korea |
| Event Dates: | 11.10.2005-13.10.2005 |
| DOI: | 10.1007/11562214_67 |
| Corresponding Links: | |
| Abstract: | We present a new method for computing semantic relatedness of concepts. The method relies solely on the structure of a conceptual network and eliminates the need for performing additional corpus analysis. The network structure is employed to generate artificial conceptual glosses. They replace textual definitions proper written by humans and are processed by a dictionary based metric of semantic relatedness [1]. We implemented the metric on the basis of GermaNet, the German counterpart of WordNet, and evaluated the results on a German dataset of 57 word pairs rated by human subjects for their semantic relatedness. Our approach can be easily applied to compute semantic relatedness based on alternative conceptual networks, e.g. in the domain of life sciences. |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Ubiquitous Knowledge Processing |
| Date Deposited: | 20 Nov 2008 08:24 |
| Last Modified: | 05 Dec 2024 11:21 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Send an inquiry |
Options (only for editors)
 |
Show editorial Details |
Drucken
Impressum