Abstract
Word sense discrimination is an unsupervised clustering problem, which seeks to discover which instances of a word/s are used in the same meaning. This is done strictly based on information found in raw corpora, without using any sense tagged text or other existing knowledge sources. Our particular focus is to systematically compare the efficacy of a range of lexical features, context representations, and clustering algorithms when applied to this problem.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-04) |
| Subtitle of host publication | Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) |
| Pages | 964-965 |
| Number of pages | 2 |
| State | Published - Dec 9 2004 |
| Event | Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) - San Jose, CA, United States Duration: Jul 25 2004 → Jul 29 2004 |
Other
| Other | Proceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) |
|---|---|
| Country/Territory | United States |
| City | San Jose, CA |
| Period | 7/25/04 → 7/29/04 |