There is massive information hidden in the biomedical literature in the form of scientific publications, book chapters, conference reports, etc. This information is growing exponentially with the speed exceeding Moore__ampersandsignrsquo;s Law i.e. observations double in every two years. It is therefore not possible for researchers and practitioners to automatically extract and relate information from different written resources. Also the data present in the written recourses is unstructured i.e. free-text therefore it becomes very arduous and exorbitant to obtain annotated material for its literature. So in order to overcome these problems Natural Language Processing (NLP) and Unsupervised Learning approaches are used. Natural Language Processing approach is the part of text mining which is the discovery by computer of new, previously unknown information by automatically extracting and relating information from different written resources to reveal the otherwise __ampersandsignlsquo;hidden__ampersandsignrsquo; meanings. The Unsupervised Learning approach is the part of machine learning where no annotated training is necessary and it is more about exploring the data to find insights. Both these approaches can be used to find knowledge from written textual data in the form different interactions like protein-protein, gene-gene, gene-protein, etc. These approaches could also be used to develop classifiers, databases, tools or softwares which in future would automatically extract the knowledgeable information from literature, answering questions arising in the biomedical research and would also help in the development of new hypothesis. So here we discuss 53 softwares, tools and databases developed using Natural Language Processing (NLP) and unsupervised learning approaches, which are involved in plain texts analyzing and processing, categorizes current work in biomedical information and entities extraction.