A novel hybrid methodology of measuring sentence similarity
  The problem of measuring sentence similarity is an essential issue in the
natural language processing (NLP) area. It is necessary to measure the
similarity between sentences accurately. There are many approaches to measuring
sentence similarity. Deep learning methodology shows a state-of-the-art
performance in many natural language processing fields and is used a lot in
sentence similarity measurement methods. However, in the natural language
processing field, considering the structure of the sentence or the word
structure that makes up the sentence is also important. In this study, we
propose a methodology combined with both deep learning methodology and a method
considering lexical relationships. Our evaluation metric is the Pearson
correlation coefficient and Spearman correlation coefficient. As a result, the
proposed method outperforms the current approaches on a KorSTS standard
benchmark Korean dataset. Moreover, it performs a maximum of 65% increase than
only using deep learning methodology. Experiments show that our proposed method
generally results in better performance than those with only a deep learning
model.
