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Table 3 Word sense disambiguation results

in Statistical Models for the Induction and Use of Selectional Preferences
by Marc Light, Warren Greiff 2002
"... In PAGE 11: ... Each system was trained on the training set and then used to assign a word sense to the objects in the test set. Table3 presents the accuracy of each system on word sense disambiguation. The random method is simply to randomly pick a sense and is included as a baseline for comparison.... ..."
Cited by 9

Table 3 Word sense disambiguation results

in Statistical Models for the Induction and Use of Selectional Preferences
by Marc Light, Warren Greiff 2002
"... In PAGE 11: ... Table3 presents the accuracy of each system on word sense disambiguation. The random 346 method is simply to randomly pick a sense and is included as a baseline for comparison.... ..."

Table 3. Word sense disambiguation results.

in Enriching very large ontologies using the WWW
by Eneko Agirre, Olatz Ansa, Eduard Hovy, David Martínez 2000
"... In PAGE 4: ...nknown switch argument.(. We chose a window of 50 words preceding the target word and 50 words following it. Table3 shows the results for the selected nouns. The number of senses attested in SemCor4 and the number of occurrences of the word in SemCor are also presented.... In PAGE 5: ... The signature results for the original sense distinctions (cf. Table3 ) are shown in the second column. The results for the signature and hierarchy combination are shown according to the sense-distinctions: the fine column shows the results using the hierarchy for the finest sense distinctions, the medium column corresponds to the medium sized clusters, and the coarse level corresponds to the coarsest clusters, i.... ..."
Cited by 63

Table 19: Results disambiguating coarse senses.

in Analysis of Supervised Word Sense Disambiguation Systems
by n.n.
"... In PAGE 27: ....5. Coarse Senses: It has been arg task more dif semantic file taken as a sing and the coarse In case any appl these senses both in Semcor and DSO. The results are shown in Table19 for the words in Set B. At this level the results on both corpora reach 83% of precision.... ..."

Table 3. Experimental results of word sense disambiguation in COBALT-KJJ (%)

in Republic of KOREA
by Sin-jae Kang
"... In PAGE 7: ...ccurring word patterns in disambiguating word senses. This is a general method without an ontology. The third, ONTO, shows the results of our WSD method using the ontology. The experimental results are compared with each other in Table3 . In these experiments, the ONTO method achieved a 9% improvement over the LEX method.... ..."

Table 3: Mean contribution of algorithms to word sense disambiguation (smoothing) and ranking

in BMC Medical Informatics and Decision Making 2007, 7:3 doi:10.1186/1472-6947-7-3
by Biomed Central, Herman D Tolentino, Michael D Matters, Wikke Walop, Barbara Law, Wesley Tong, Fang Liu, Paul Fontelo, Katrin Kohl, Daniel C Payne Open Access, Paul Fontelo, Katrin Kohl, Daniel C Payne 2006
"... In PAGE 8: ... quot;retch, quot; the spell checker detects the misspell- ing and generates a word list using the insertion algo- rithm. Table3 shows the mean contribution of the word sense disambiguation algorithms. Here, the N-Gram algo- rithm contributed the highest proportion to smoothing the Levenshtein score.... ..."

Table 3. Word sense disambiguation results.

in Enriching very large ontologies using the WWW
by Eneko Agirre, Olatz Ansa, Eduard Hovy, David Martínez 2000
"... In PAGE 4: ... The algorithm to use these lists is the same as for the topic signatures. Table3 shows the results for the selected nouns. The number of senses attested in SemCor3 (#s) and the number of occurrences of the word in SemCor (#occ) are also presented.... In PAGE 5: ... The signature results for the original sense distinctions (cf. Table3 ) are shown in the second column. The results for the signature and hierarchy combination are shown according to the sense-distinctions: the fine column shows the results using the hierarchy for the finest sense distinctions, the medium column corresponds to the medium sized clusters, and the coarse level corresponds to the coarsest clusters, i.... ..."
Cited by 63

Table 9: Mikrokosmos Word Sense Disambiguation Results

in Hunter-Gatherer: Applying Constraint Satisfaction, Branch-and-Bound and Solution Synthesis to Computational Semantics
by Stephen Beale, Jaime Carbonell, Robert Frederking, Victor Raskin
"... In PAGE 85: ...5. Using Hunter-Gatherer in Semantic Analysis - The Results Table9 shows the latest disambiguation results from Mikrokosmos. These are results from... ..."

Table 3. Word sense disambiguation results.

in Enriching very large ontologies using the WWW
by Eneko Agirre, Olatz Ansa, Eduard Hovy, David Martínez 2000
"... In PAGE 4: ... The algorithm to use these lists is the same as for the topic signatures. Table3 shows the results for the selected nouns. The number of senses attested in SemCor3 (#s) and the number of occurrences of the word in SemCor (#occ) are also presented.... In PAGE 5: ... The signature results for the original sense distinctions (cf. Table3 ) are shown in the second column. The results for the signature and hierarchy combination are shown according to the sense-distinctions: the fine column shows the results using the hierarchy for the finest sense distinctions, the medium column corresponds to the medium sized clusters, and the coarse level corresponds to the coarsest clusters, i.... ..."
Cited by 8

Table 3: Word Sense Disambiguation Results

in Hiding a Semantic Hierarchy in a Markov Model
by Steven Abney Att, Steven Abney 1999
Cited by 16
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