Examples of the present invention provide a word sense embedding apparatus and a method using a lexical semantic network, the apparatus and method being capable of: generating processing data by using a word list to be learned in a lexical semantic network and word sense data of words to be learned (for example, dictionary definitions, hyperonyms, antonyms, and the like); and performing learning by using the generated processing data through a negative-sampling and a feature mirror model, which is a modified skip-gram model, of word sense embedding, thereby enabling a semantic relationship and correlation between words to be expressed as a vector. In addition, examples of the present invention provide a homograph discrimination apparatus and method using a lexical semantic network and word embedding, the apparatus and method being capable of: learning through word embedding learning using a word list to be learned from various resources (for example, a corpus, a standard unabridged dictionary, and a lexical semantic network), a converted corpus, and the word sense data; and accurately discriminating homographs with respect to a non-learning pattern by comparing the similarity between the homograph and an adjacent syntactic word so as to distinguish the homographs.
WORD SENSE EMBEDDING APPARATUS AND METHOD USING LEXICAL SEMANTIC NETWORK, AND HOMOGRAPH DISCRIMINATION APPARATUS AND METHOD USING LEXICAL SEMANTIC NETWORK AND WORD EMBEDDING