Learning Approach to Automatic Synonym Identification based on Distributional Features Masato Higiwara Graduate School of Information Science Nagoya University Proceedings of the ACL-08: HLT Student Research Workshop (Companion Volume), p1-6, Columbus, June 2008.
semantic relatedness of word in NLP tasks. • This paper use a novel approach to synonym identification based on supervised learning and distributional features. • The F1 of this evaluation experiment increase over 120%, compared with the conventional classification.
words by the commonality of context. • Syntactic pattern such as “such X as Y” and “Y and other X”, and extract hyponym relation of X and Y. • This paper , they re-formalize synonym acquisition as a classification which classifies word pairs into synonym/non-synonym.
words and relations which are on the dependency path. • In the experiment, they limited the maximim length of syntactic path to five. • Define and calculate the pattern based feature, which corresponds to the syntactic pattern.
section (1994) of consisting 46,000 documents, 922,000 sentences, and 30 million words. – Apply feature selection and reduce the dimensionality. • Supervised learning – The example set E end up with 2,148 positive and 13,855 negative examples. – Divide to conduct five-fold cross calidation. SVM was adopted for ml and RBF as the kernel.