Abstract: A name entity extraction technique using language models is provided. A general language model is provided for the natural language understanding domain. A language model is also provided for each name entity. The name entity language models are added to the general language model. Each language model is considered a state. Probabilities are applied for each transition within a state and between each state. For each word in an utterance, the name extraction process determines a best current state and a best previous state. When the end of the utterance is reached, the process traces back to find the best path. Each series of words in a state other than the general language model state is identified as a name entity. A technique is provided to iteratively extract names and retrain the general language model until the probabilities do not change.

Abstract: A name entity extraction technique using language models is provided. A general language model is provided for the natural language understanding domain. A language model is also provided for each name entity. The name entity language models are added to the general language model. Each language model is considered a state. Probabilities are applied for each transition within a state and between each state. For each word in an utterance, the name extraction process determines a best current state and a best previous state. When the end of the utterance is reached, the process traces back to find the best path. Each series of words in a state other than the general language model state is identified as a name entity. A technique is provided to iteratively extract names and retrain the general language model until the probabilities do not change.