| dc.description.abstract | The word embedding model is a technique that utilizes contextual words to generate a vector for each word, which is called word embedding. Usually, we can use the cosine similarity between a pair of word embeddings
to calculate the relevance score between the two words. However, it is difficult to use word embeddings to detect the hypernym-hyponym relationship between two words. In addition, being an asymmetric semantic relationship, even when given a pair of vocabularies with a hypernym-hyponym relationship, it is challenging to apply general distance measures, which
are often symmetric, to determine which is the hypernym and which is the
hyponym.
This thesis proposes a model based on a BERT pre-trained model with auxiliary sentences to determine the hypernym-hyponym relationship of a pair of words. The entire process is consisted of two tasks. First, when given a pair of words, the model determines whether the word pair has a hypernym-hyponym relationship. Then, if the result is true, the model proceeds to the second task: distinguishing the hypernym and the hyponym. Experimental results show that two approaches to construct auxiliary sentences, BERT+Q and BERT+Q+PosNeg, can effectively accomplish both tasks. | en_US |