Classification Performance of Bio-Marker and Disease Word using Word Representation Models

Abstract

One of the most important processesin a machine learning-based natural language processing module is to represent words by inputting the module. This can be accomplished by representing words in one-hot form with a large vector size without applying the concept of semantic similarity between words, or by word representation (word embedding) with vectors to represent lexical similarity. This has attracted keen research interest by improving the performance of several natural language processing modelssuch as syntactic parsing and sentiment analysis (also known as opinion mining). In this study, classification performance of Word2Vec, canonical correlation analysis (CCA), and GloVeare tested on a corpus that established using the titles and abstractsof 204,674biomedical articles published in PubMed. Categories include disease name, disease symptom, and ovarian cancer marker.Ovarian cancer markers were used as biomarkers.The classification performance of each word representation model for each category is visualized by mapping the results in two-dimensional word representations using t-distributed stochastic neighbor embedding (t-SNE).