1. A machine learning method for computer question-answering, comprising: receiving a plurality of questions to be trained, and obtaining a candidate answer set for each of said questions; determining a part of said questions to which said candidate answer sets include correct answers, and using first feature sets of said candidate answers to which said part of said questions correspond to form a first input training data set, wherein said first feature sets comprises at least one of: a matching degree of a type of said candidate answer and said question to be trained; a literal similarity between the text around said candidate answer and the text of said question to be trained; a matching degree of a time feature of said candidate answer and the time appearing in said question to be trained; and a matching degree of a geographic location of said candidate answer and a geographic location appearing in said question to be trained; performing machine learning on said first input training data set to obtain a first mathematical model, and using said first mathematical model to compute a first confidence degree that said candidate answer is a correct answer based on said first feature set; computing first confidence degrees of said candidate answers of all of said questions to be trained, and for each question, extracting a second feature set related to said first confidence degrees and said first feature set; and performing machine learning on a second input training data set formed by said second feature sets of said plurality of questions to be trained, to obtain a second mathematic model, and using said second mathematical model to compute a second confidence degree that said candidate answer is a correct answer based on said second feature set.
1. A machine learning method for computer question-answering, comprising: receiving a plurality of questions to be trained, and obtaining a candidate answer set for each of said questions; determining a part of said questions to which said candidate answer sets include correct answers, and using first feature sets of said candidate answers to which said part of said questions correspond to form a first input training data set, wherein said first feature sets comprises at least one of: a matching degree of a type of said candidate answer and said question to be trained; a literal similarity between the text around said candidate answer and the text of said question to be trained; a matching degree of a time feature of said candidate answer and the time appearing in said question to be trained; and a matching degree of a geographic location of said candidate answer and a geographic location appearing in said question to be trained; performing machine learning on said first input training data set to obtain a first mathematical model, and using said first mathematical model to compute a first confidence degree that said candidate answer is a correct answer based on said first feature set; computing first confidence degrees of said candidate answers of all of said questions to be trained, and for each question, extracting a second feature set related to said first confidence degrees and said first feature set; and performing machine learning on a second input training data set formed by said second feature sets of said plurality of questions to be trained, to obtain a second mathematic model, and using said second mathematical model to compute a second confidence degree that said candidate answer is a correct answer based on said second feature set. 2. The method according to claim 1 , wherein said first feature set further comprises information about whether said candidate answer is a correct answer.