In order to provide a non-factoid question answering system with improved precision, the question answering system (160) includes: a candidate retrieving unit (222), responsive to a question, extracting answer candidates from a corpus storage (178); a feature vector generating unit (232) for generating features from combinations of a question with each of the answer candidates; SVMs (176) trained to calculate a score of how correct a combination of the question with an answer candidate is, upon receiving the feature vector therefor; and an answer ranker unit (234) outputting the answer candidate with the highest calculated score as the answer. The features are generated on the basis of the results of morphological analysis and parsing of the question, a phrase in the question evaluated as being positive or negative as well as its polarity, and the semantic classes of nouns in the features.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-factoid question-answering system, receiving an input of a non-factoid question and generating an answer to the question, to be connected to corpus storage means for storing a corpus composed of computer-readable documents of a language as an object of processing, comprising: candidate retrieving means, responsive to an input of a question, for searching and extracting a plurality of answer candidates to the question from said corpus storage means; feature generating means, responsive to an input of a question, for generating prescribed features from combinations of the question and each of the answer candidates stored in said candidate retrieving means; scoring means trained in advance to calculate, upon receiving features generated by said feature generating means from a combination of an answer candidate and a question, a score indicating degree of the answer candidate in the combination being a correct answer to the question in the combination; and answer selecting means for outputting, based on the scores calculated by said scoring means for the combinations of the question and each of the answer candidates stored in said answer candidates, an answer candidate having the highest probability of a correct answer to the question as an answer to said question; wherein each of the answer candidates retrieved by said candidate retrieving means has assigned thereto information necessary for said feature generating means to generate said features; said feature generating means includes parsing means for performing morphological analysis and parsing on said question and outputting morphological information and syntactic information, and evaluating means for specifying, from said question, a phrase classified to a first evaluation class and a phrase classified to a second evaluation class, in accordance with a certain evaluation reference, said first and second evaluation classes having assigned thereto mutually opposite evaluation polarities; said system further comprising feature generating means for generating said features from the combinations of said question and each of said answer candidates, based on the result of analysis by said parsing means, locations of phrases specified by said evaluating means and their evaluation class polarities, and the information for generating features assigned to said answer candidate. 2. The non-factoid question-answering system according to claim 1, wherein said feature generating means further includes semantic class converting means for classifying each noun included in the output from said parsing means to any of a plurality of semantic word classes prepared in advance, and converting the noun to its corresponding semantic class; and said feature generating means includes first means for generating said features from the combinations of said question and each of said answer candidates, based on the result of analysis by said parsing means, locations of phrases specified by said evaluating means and their evaluation class polarities, an output of said parsing means after conversion by said semantic class converting means, and the information for generating features assigned to said answer candidate. 3. The non-factoid question-answering system according to claim 2, wherein the feature generated by said first means based on the locations of phrases specified by said evaluating means and their evaluation class polarities includes information indicating whether or not the evaluation class polarity of a phrase in the question agrees with the evaluation class polarity of a phrase in the answer candidate. 4. The non-factoid question-answering system according to claim 2, wherein the feature generated by said first means includes a feature obtained from the output of said parsing means not using the output of said evaluating means or the output from said semantic class converting means, a feature obtained by combining the output of said parsing means with the output of said evaluating means, and a feature obtained by combining the output of said parsing means with the output of said semantic class converting means. 5. The non-factoid question-answering system according to claim 2, wherein the feature generated by said first means includes a feature obtained by combining the output of said parsing means with the output of said evaluating means, and further with the output of said semantic class converting means. 6. A computer program realizing, by a computer, a non-factoid question-answering system, receiving an input of a non-factoid question and generating an answer to the question; said computer is connected to corpus storage means for storing a corpus composed of computer-readable documents of a language as an object of processing; said computer program causing said computer to function as: candidate retrieving means, responsive to an input of a question, for searching and extracting a plurality of answer candidates to the question from said corpus storage means; feature generating means, responsive to an input of a question, for generating prescribed features from combinations of the question and each of the answer candidates stored in said candidate retrieving means; scoring means trained in advance to calculate, upon receiving features generated by said feature generating means from a combination of an answer candidate and a question, a score indicating degree of the answer candidate in the combination being a correct answer to the question in the combination; and answer selecting means for outputting, based on the scores calculated by said scoring means for the combinations of the question and each of the answer candidates stored in said answer candidates, an answer candidate having the highest probability of a correct answer to the question as an answer to said question; wherein each of the answer candidates retrieved by said candidate retrieving means has assigned thereto information necessary for said feature generating means to generate said features; a program part causing said computer to function as said feature generating means causes said computer to function as parsing means for performing morphological analysis and parsing on said question and outputting morphological information and syntactic information, and evaluating means for specifying, from said question, a phrase classified to a first evaluation class and a phrase classified to a second evaluation class, in accordance with a certain evaluation reference, said first and second evaluation classes having assigned thereto mutually opposite evaluation polarities; said computer program further causing said computer to function as feature generating means for generating said features from the combinations of said question and each of said answer candidates, based on the result of analysis by said parsing means, locations of phrases specified by said evaluating means and their evaluation class polarities, and the information for generating features assigned to said answer candidate.