Abstract
In computational linguistics, meaning disambiguation is an open problem of natural language processing in the form of the process of identifying the meaning of the word polysemy used in a sentence. Resolving this problem, among others, has an impact on search engine relevance, anaphoric solving, coherence or cohesion, and inference or conclusion. Therefore, a study is needed that studies to find the meaning of a correct word on a topic. So that it affects the topics discussed in a sentence to find the true meaning. In this study, we focused on finding the meaning of words in a corpus-based sentence using word2vec and wu palmer. The word2vec algorithm is used to construct word vectors contained in sentences and wu palmer as an addition to new words that are not contained in the corpus, by assessing hypernym, meronym, and hyponym between words in sentences. The experimental results show that by adding a new word using wu palmer on corpus it can increase the precision value of 0.8232 in an introduction to a sentence contained in a topic, compared to not using the addition of a new word.
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