7. A named-entity detection method comprising: detecting a candidate-named-entity based on an initial learning example and feature information regarding morphemes constituting an inputted sentence and providing a tagged sentence including the detected candidate-named-entity by a candidate-named-entity extraction module, wherein the candidate-named entity extraction module is a hardware module; finally determining whether the candidate-named-entity included in the provided sentence is a valid named-entity, based on pre-stored named-entity dictionary and rule by learning-example regeneration module, and providing the sentence as a learning example, based on a determination result, so that a probability of candidate-named-entity detection is gradually updated by the learning-example-regeneration module, wherein, in the detecting, a class of each morpheme is determined by using a learning model based on the feature information, and a tagged sentence is provided after adding determined class information as a tag, wherein, in the providing, the learning example is reflected in the learning model as a negative example when the rule is violated, wherein the probability of detecting the named-entity of the learning-example-regeneration module is gradually updated in the learning module when the learning example is a negative example, wherein the feature information comprises a word feature, a part-of-speech feature, a concept feature, an adjacent verb feature, and an adjacent verb concept feature, and wherein the concept feature comprises ontological concept information of a current morpheme as well as three morphemes before and after the current morpheme.
12. The named-entity detection method of claim 7 , wherein, in the providing, the sentence including the candidate-named-entity is provided as a positive learning example when the candidate-named-entity is included in the named-entity dictionary.