Applying Name Entity Recognition to Informal Text

Although Name Entity Recognition (NER) has been a well-studied problem in recent years, it is seldom applied to informal document, such as E-mail message and Newsgroup postings. Unlike the formal text, which is well-structured and with seldom error, the name entities are much more difficult to recognize in informal one. The key problems for informal text are that it has unstructured properties, more grammatical error, and more spelling error. All of these properties will degrade the performance of the existent classifiers and well-designed features which are suitable for original NER. In this project, we are going to apply two approaches, Maximum Entropy Classifier (MaxEnt ) and Conditional Random Field (CRF), which are often used for formal text NER, to informal text NER. We do some experiment to show if they are still good for the informal task. We also focus on how to extract efficient and effective features especially for informal text.