lr,0-1-I05-4007,ak contributed to the <term> scores </term> . <term> Parallel wordnets </term> built upon correspondences between
other,7-1-I05-4007,ak correspondences between different <term> languages </term> can play a crucial role in <term> multilingual
tech,14-1-I05-4007,ak languages </term> can play a crucial role in <term> multilingual knowledge processing </term> . Since there is no <term> homomorphism
other,4-2-I05-4007,ak processing </term> . Since there is no <term> homomorphism </term> between pairs of <term> monolingual
lr,8-2-I05-4007,ak homomorphism </term> between pairs of <term> monolingual wordnets </term> , we must rely on <term> lexical semantic
model,15-2-I05-4007,ak monolingual wordnets </term> , we must rely on <term> lexical semantic relation ( LSR ) mappings </term> to ensure <term> conceptual cohesion
other,24-2-I05-4007,ak relation ( LSR ) mappings </term> to ensure <term> conceptual cohesion </term> . In this paper , we propose and
lr,12-3-I05-4007,ak implement a model for bootstrapping <term> parallel wordnets </term> based on one <term> monolingual wordnet
lr,17-3-I05-4007,ak parallel wordnets </term> based on one <term> monolingual wordnet </term> and a set of <term> cross-lingual lexical
model,23-3-I05-4007,ak monolingual wordnet </term> and a set of <term> cross-lingual lexical semantic relations </term> . In particular , we propose a set
model,8-4-I05-4007,ak In particular , we propose a set of <term> inference rules </term> to predict <term> Chinese wordnet structure
other,12-4-I05-4007,ak <term> inference rules </term> to predict <term> Chinese wordnet structure </term> based on <term> English wordnet </term>
lr,17-4-I05-4007,ak Chinese wordnet structure </term> based on <term> English wordnet </term> and <term> English-Chinese translation
other,20-4-I05-4007,ak on <term> English wordnet </term> and <term> English-Chinese translation relations </term> . We show that this model of <term>
tech,6-5-I05-4007,ak </term> . We show that this model of <term> parallel wordnet building </term> is effective and achieves higher <term>
measure(ment),14-5-I05-4007,ak </term> is effective and achieves higher <term> precision </term> in <term> LSR prediction </term> . <term>
tech,16-5-I05-4007,ak achieves higher <term> precision </term> in <term> LSR prediction </term> . <term> Taiwan Child Language Corpus
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