Entities are real world elements or objects such as Person names, Organization names, Product names, Location names. Entities are often reffered to as Named Entities. Entity extraction refers to automatic identification of named entities in a text document. Given a text document, entities such as Person names, Organization names, Location names, Product names are identified and tagged. Identification of named entities is very important for several higher language technology systems such as information extraction systems, machine translation systems, and cross-lingual information access systems.

    Over the past decade, Indian language content on various media types such as websites, blogs, email, chats has increased significantly. And it is observed that with the advent of smart phones more people are using social media such as twitter, facebook to comment on people, products, services, organizations, goverments. Thus we see content growth is driven by people from non-metros and small cities who are mostly comfortable in their own mother tongue rather than English. Though still this Indian language content is only a fraction of the English content. The growth of Indian language content is expected to increase by more than 70% every year. Hence there is great need to process this huge data automatically. Especially companies are interested to ascertain public view on their products and processes. This requires natural language processing software systems which identify entities, identification of associations or relation between entities. Hence an automatic Entity extraction system is required.

The objectives of this evaluation exercise are:

• Creation of benchmark data for Entity Extraction in Indian language Social Media text.
• Encourage researchers to develop Named Entity Recognition (NER) systems in Social Media text.
• Providing oppurtunity to researchers to have comparison of different machine learning techniques.