Abstract
Feature sparseness is one of the main causes for Word Sense Disambiguation (WSD) systems to fail, as it increases the probability of incorrect predictions. In this work, we present a WSD method to overcome this problem by using an automatically-created thesaurus to append related words to a specific context, in order to improve the effectiveness of candidate selection for an ambiguous word. We treat the context as a vector of words taken from sentences, and expand it with words from the thesaurus according to their mutual relatedness. Our results suggest that the method performs disambiguation with high precision.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE 6th International Conference on Semantic Computing, ICSC 2012 |
| Pages | 339-341 |
| Number of pages | 3 |
| DOIs | |
| Publication status | Published - 2012 |
| Externally published | Yes |
| Event | 6th IEEE International Conference on Semantic Computing, ICSC 2012 - Palermo Duration: 2012 Sep 19 → 2012 Sep 21 |
Other
| Other | 6th IEEE International Conference on Semantic Computing, ICSC 2012 |
|---|---|
| City | Palermo |
| Period | 12/9/19 → 12/9/21 |
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ASJC Scopus subject areas
- Software
Cite this
A context expansion method for supervised word sense disambiguation. / Tacoa, Francisco; Bollegala, Danushka; Ishizuka, Mitsuru.
Proceedings - IEEE 6th International Conference on Semantic Computing, ICSC 2012. 2012. p. 339-341 6337125.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A context expansion method for supervised word sense disambiguation
AU - Tacoa, Francisco
AU - Bollegala, Danushka
AU - Ishizuka, Mitsuru
PY - 2012
Y1 - 2012
N2 - Feature sparseness is one of the main causes for Word Sense Disambiguation (WSD) systems to fail, as it increases the probability of incorrect predictions. In this work, we present a WSD method to overcome this problem by using an automatically-created thesaurus to append related words to a specific context, in order to improve the effectiveness of candidate selection for an ambiguous word. We treat the context as a vector of words taken from sentences, and expand it with words from the thesaurus according to their mutual relatedness. Our results suggest that the method performs disambiguation with high precision.
AB - Feature sparseness is one of the main causes for Word Sense Disambiguation (WSD) systems to fail, as it increases the probability of incorrect predictions. In this work, we present a WSD method to overcome this problem by using an automatically-created thesaurus to append related words to a specific context, in order to improve the effectiveness of candidate selection for an ambiguous word. We treat the context as a vector of words taken from sentences, and expand it with words from the thesaurus according to their mutual relatedness. Our results suggest that the method performs disambiguation with high precision.
UR - http://www.scopus.com/inward/record.url?scp=84870716767&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870716767&partnerID=8YFLogxK
U2 - 10.1109/ICSC.2012.27
DO - 10.1109/ICSC.2012.27
M3 - Conference contribution
AN - SCOPUS:84870716767
SN - 9780769548593
SP - 339
EP - 341
BT - Proceedings - IEEE 6th International Conference on Semantic Computing, ICSC 2012
ER -