1. A method for automatically detecting errors in machine translation using a parallel corpus, comprising: analyzing, by a processor, morphemes of a target language sentence in the parallel corpus and a machine-translated target language sentence, corresponding to a source language sentence, to classify the morphemes into words; aligning by words and decoding, respectively, a group of the source language sentence and the machine-translated target language sentence, and a group of the source language sentence and the target language sentence in the parallel corpus; classifying by types errors in the machine-translated target language sentence by making a comparison, word by word, between the decoded target language sentence in the parallel corpus and the decoded machine-translated target language sentence; and computing error information in the machine-translated target language sentence by examining a frequency of occurrence of said classified error types, and wherein said classifying the errors by types includes: decoding by words the target language sentence in the parallel corpus and the machine-translated target language sentence, respectively; producing respective decoded alignment tables on the basis of the decoded information of the words in the target language sentence in the parallel corpus and the machine-translated target language sentence that match respective words of the source language sentence; comparing the word information of the target language sentence in the parallel corpus and the machine-translated target language sentence, in the respective decoded alignment tables, to detect errors in the machine-translated target language sentence; and classifying the detected errors by types on the basis of causes of the errors, wherein said classifying the detected errors by types includes searching for words in the decoded alignment tables of the target language sentence in the parallel corpus and the machine-translated target language sentence which match respective words of the source language sentence; and classifying as OOV(out of vocabulary) word error or target word error of the machine-translated target language sentence if the matched word is present in the decoded alignment table of the target language sentence in the parallel corpus in the target language but not in the decoded alignment table of the machine-translated target language sentence, wherein said classifying the detected errors by types includes searching for words in the decoded alignment tables of the target language sentence in the parallel corpus and the machine-translated target language sentence that match respective words of the source language sentence; and classifying as inflected phrase pattern error or conjunction pattern error of the machine-translated target language sentence if alignment sequences of the matched words are inconsistent, wherein said classifying the detected errors by types includes classifying as knowledge error or clause division error of the machine-translated target language sentence if terminology information of respective words in the decoded alignment tables of the target language sentence in the parallel corpus and the machine-translated target language sentence that match respective words of the source language sentence is inconsistent, wherein said classifying the detected errors by types includes classifying as part of speech tag error of the the machine-translated target language sentence if part of speech information of respective words in the decoded alignment tables of the target language sentence in the parallel corpus and the machine-translated target language sentence that match respective words of the source language sentence is inconsistent, wherein said classifying the detected error by types includes classifying as tense error or syntax analysis error of the machine-translated target language if tense information of respective words in the decoded alignment tables of the target language sentence in the parallel corpus and the machine-translated target language sentence that match respective words of the source language sentence is inconsistent.