The Web of Data provides abundant knowledge wherein objects or entities are described by means of properties and their relationships with other objects or entities. This knowledge is used extensively by the research community for Information Extraction tasks such as Named Entity Recognition (NER) and Linking (NEL) to make sense of data. Named entities can be identified from a variety of textual formats which are further linked to corresponding resources in the Web of Data. These tasks of entity recognition and linking are, however , cast as distinct problems in the state-of-the-art, thereby, overlooking the fact that performance of entity recognition affects the performance of entity linking. The focus of this paper is to improve the performance of entity recognition on a particular textual format, viz, microblog posts by disambiguating the named entities with resources in a Knowledge Base (KB). We propose an unsupervised learning approach to jointly improve the performance of entity recognition and, thus, the whole system by leveraging the results of disambiguated entities.