Abstract
This paper investigates the determination of semantic similarity by the incorporation of structural semantic knowledge from a lexical database and the learning ability of neural networks. The lexical database is assumed to be organised in a hierarchical structure. The extracted lexical knowledge contains the relative location of the concerned words in the lexical hierarchy. The neural network then processes available lexical knowledge to provide semantic similarity for words. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed method is effective in measuring semantic similarity between words.
| Original language | English |
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| Publisher | Springer |
| Number of pages | 6 |
| Volume | 2412 |
| ISBN (Print) | 3-540-44025-9 |
| Publication status | Published - 2002 |
Publication series
| Name | LECTURE NOTES IN COMPUTER SCIENCE |
|---|---|
| Publisher | SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY |
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Cite this
Li, Y., Bandar, Z., & Mclean, D. (2002). Measuring semantic similarity between words using lexical knowledge and neural networks. (LECTURE NOTES IN COMPUTER SCIENCE). Springer.