Using word embeddings for library and information science research

Abstract

In the research field of library and information science (LIS), pattern recognition and machine learning approaches have often been adopted to analyze massive amounts of textual data in digital libraries. In recent natural language processing, word embeddings have attracted much attention because they can capture semantic meanings and contexts. This paper presents a short survey of how word embeddings have been used in LIS research, especially focusing on articles published in LIS journals between 2009 and 2019. Our simple bibliographic analysis showed that at least 15 LIS journal papers used word embeddings. These 15 papers were briefly described under the following categories: knowledge extraction and visualization from scholarly data, classification in scholarly data, and others that focus on more general corpora.