Comparative Analysis of Word Embeddings for Capturing Word Similarities

Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.Comment: Part of the 6th International Conference on Natural Language Processing (NATP 2020

The Impact of Leadership on Health – A Preliminary Discussion

Contemporary leaders are confronted with unrelenting demands while setting visions, goals, and objectives for organizations. Empirical studies have shown that leaders are increasingly faced with high levels of stress which, in turn, have an impact on the health of individuals, groups, and entire organizational entities. There is a particular concern for the individual health of leaders since leaders play a particularly important role in the success and future survival of firms. This research paper endeavors to address some of the pertinent issues of the concept of leadership as they specifically relate to and impact health.leadership; health; organizations; leaders.