  Named entities in text documents are the names of people, organization,
location or other types of objects in the documents that exist in the real
world. A persisting research challenge is to use computational techniques to
identify such entities in text documents. Once identified, several text mining
tools and algorithms can be utilized to leverage these discovered named
entities and improve NLP applications. In this paper, a method that clusters
prominent names of people and organizations based on their semantic similarity
in a text corpus is proposed. The method relies on common named entity
recognition techniques and on recent word embeddings models. The semantic
similarity scores generated using the word embeddings models for the named
entities are used to cluster similar entities of the people and organizations
types. Two human judges evaluated ten variations of the method after it was run
on a corpus that consists of 4,821 articles on a specific topic. The
performance of the method was measured using three quantitative measures. The
results of these three metrics demonstrate that the method is effective in
clustering semantically similar named entities.
