A Survey of Stochastic and Gazetteer Based Approaches for Named Entity Recognition

The task of identifying proper names of people, organizations, locations, or other entities is a subtask of information extraction from natural language documents. This paper presents a survey of techniques and methodologies that are currently being explored to solve this difficult subtask. After a brief review of the difficulties and challenges of the task, as well as a look at previous conventional approaches, the focus will shift to a comparison of stochastic and gazetteer based approaches. Several machine-learning approaches are identified and explored, as well as a discussion of knowledge acquisition relevant to recognition. This paper will show that applications that require named entity recognition will be served best by some combination of knowledgebased and non-deterministic approaches.