TY - GEN
T1 - Augmenting word space models for Word Sense Discrimination using an automatic thesaurus
AU - Calvo, Hiram
N1 - Funding Information:
This work has been partially supported by Strategic Information and Communications R&D Promotion Programme (SCOPE) of Ministry of Internal Affairs and Communications, Japan.
PY - 2008
Y1 - 2008
N2 - This paper presents an algorithm for Word Sense Discrimination that divides the global representation of a word into a number of classes by determining for any two occurrences whether they belong to the same sense or not. We rely on the notion that words that are used in similar contexts will have the same or a closely related meaning, thus, given a target word, we group its dependency co-occurrences in a Word Space Model. Each cluster represents a distinct meaning or sense of that word. We experiment with augmenting the bag of words of each cluster of co-occurrences, the dictionary of sense definition, and augmenting both. Then we count the number of intersections of each word of the bag of clustered senses and the bag of the dictionary of senses following the Lesk method. We find an increase in recall and a decrease in precision when augmenting. However, the best resulting F-measure is for the option of augmenting the both dictionary of senses and the bag of words from the clusters.
AB - This paper presents an algorithm for Word Sense Discrimination that divides the global representation of a word into a number of classes by determining for any two occurrences whether they belong to the same sense or not. We rely on the notion that words that are used in similar contexts will have the same or a closely related meaning, thus, given a target word, we group its dependency co-occurrences in a Word Space Model. Each cluster represents a distinct meaning or sense of that word. We experiment with augmenting the bag of words of each cluster of co-occurrences, the dictionary of sense definition, and augmenting both. Then we count the number of intersections of each word of the bag of clustered senses and the bag of the dictionary of senses following the Lesk method. We find an increase in recall and a decrease in precision when augmenting. However, the best resulting F-measure is for the option of augmenting the both dictionary of senses and the bag of words from the clusters.
UR - http://www.scopus.com/inward/record.url?scp=52149096944&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85287-2_10
DO - 10.1007/978-3-540-85287-2_10
M3 - Contribución a la conferencia
SN - 3540852867
SN - 9783540852865
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 100
EP - 107
BT - Advances in Natural Language Processing - 6th International Conference, GoTAL 2008, Proceedings
T2 - 6th International Conference on Natural Language Processing, GoTAL 2008
Y2 - 25 August 2008 through 27 August 2008
ER -