10. A method for detecting sentence boundaries, comprising: extracting, by a computer, a sentence boundary candidate from an input document; extracting features used in two or more statistical algorithms from information on preceding and following contexts of the sentence boundary candidate; classifying whether the sentence boundary candidate is a sentence boundary or not, using the features and the two or more statistical algorithms; and extracting sentence units from the document based on a result of the classification of whether the sentence boundary candidate is a sentence boundary or not, wherein the classifying of whether the sentence boundary candidate is a sentence boundary or not comprises: classifying whether the sentence boundary candidate is a sentence boundary using the extracted features and a Maximum Entropy (ME) algorithm; classifying whether the sentence boundary candidate is a sentence boundary using the extracted features and a Support Vector Machine (SVM) algorithm; classifying whether the sentence boundary candidate is a sentence boundary using the extracted features and a Critical Random Field (CRF) algorithm; and selecting an optimum classification result from classification results of the ME-based classifier, the SVM-based classifier, and the CRF-based classifier as a final classification result, using the extracted features.
10. A method for detecting sentence boundaries, comprising: extracting, by a computer, a sentence boundary candidate from an input document; extracting features used in two or more statistical algorithms from information on preceding and following contexts of the sentence boundary candidate; classifying whether the sentence boundary candidate is a sentence boundary or not, using the features and the two or more statistical algorithms; and extracting sentence units from the document based on a result of the classification of whether the sentence boundary candidate is a sentence boundary or not, wherein the classifying of whether the sentence boundary candidate is a sentence boundary or not comprises: classifying whether the sentence boundary candidate is a sentence boundary using the extracted features and a Maximum Entropy (ME) algorithm; classifying whether the sentence boundary candidate is a sentence boundary using the extracted features and a Support Vector Machine (SVM) algorithm; classifying whether the sentence boundary candidate is a sentence boundary using the extracted features and a Critical Random Field (CRF) algorithm; and selecting an optimum classification result from classification results of the ME-based classifier, the SVM-based classifier, and the CRF-based classifier as a final classification result, using the extracted features. 11. The method of claim 10 , wherein the extracting of a sentence boundary candidate comprises extracting a punctuation mark used in sentence termination as the sentence boundary candidate.