[Object] To provide a question-answering system capable of appropriately answering to a non-factoid question. [Solution] A question-answering system 30 includes: a related document searching unit 54 responsive to a question, for taking out answer candidates from an object document storage unit 32; an answer candidate extracting unit 56 extracting plausible ones from the answer candidates; a causal relation recognizing unit 60 recognizing causal relation expression included in extracted answer candidates; causal relation relevancy determining unit 64 determining whether or not a recognized causal relation is relevant as an answer; a feature vector generating unit 66 generating a feature vector related to a combination of the question and the searched answer candidate using the results of processing up to the causal relation relevancy determining unit 64; and a feature vector generating unit 66 for calculating, when a feature vector is given, a score indicating a degree of how correct an answer candidate is as an answer to the question, for the question and the answer candidate as a source of the feature vector, and for outputting, as the answer to the question, an answer candidate having the highest score.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-factoid question-answering system, receiving a non-factoid question and generating an answer to the question, used connected to document storage means for storing a plurality of computer-readable documents, comprising: candidate search means, responsive to an input of a question, for taking out answer candidates to the question from said document storage means; feature generation means, responsive to said candidate search means having searched answer candidates, for generating, for combinations of said question and each of the answer candidates searched by said candidate search means, a set of prescribed features; and answer selection means for calculating, when a set of said features generated by said feature generation means is given, for the question and the answer candidate as a base for generating the set of features, a score representing a degree of plausibility of the answer candidate as a correct answer to the question, and outputting an answer candidate having the highest score as an answer to the question; wherein said feature generation means includes first feature calculating means for calculating, for said question and each answer candidate, a first set of features obtained from respective morpheme information and syntactic information, and second feature calculating means for selecting a causal relation expression in each of said answer candidates relevant as giving an answer to said question, and for calculating a second set of features obtained from said causal relation expression; and said set of features include said first set of features and said second set of features. 2. The non-factoid question-answering system according to claim 1, wherein said candidate search means includes classifying means, responsive to an input of said question, for classifying the question to any of a prescribed plurality of types, sentence dividing means, responsive to an input of said question, for dividing each of the documents stored in said document storage means to sets of sentences each consisting of one or more consecutive sentences and outputting the sets, and means for calculating, for each set of sentences output from said sentence dividing means, a score in accordance with a standard determined by a result of classification by said classifying means, and outputting a set of sentences having the highest score as said answer candidates. 3. The non-factoid question-answering system according to claim 1, wherein said second feature calculating means includes clue expression specifying means for specifying, in each of said answer candidates, an expression serving as a clue for specifying a causal relation expression, causal relation expression specifying means for specifying a causal relation expression consisting of a cause part and a result part of causal relation connected by a clue expression specified by said clue expression specifying means, in each of said answer candidates, match determining means for determining whether a combination of a noun and a polarity of a predicate on which the noun depends, included in the result part of said specified causal relation expression, matches a combination of a noun and a polarity of a predicate on which the noun depends, included in the question, relevancy determining means for determining whether or not the causal relation expression specified by said causal relation expression specifying means in each of said answer candidates is relevant as an answer to said question, using a result of determination by said match determining means and word matching and dependency tree matching between each of said answer candidates and said question, and means for outputting, as features of said second feature set, information representing a result of determination by said relevancy determining means. 4. The non-factoid question-answering system according to claim 3, wherein said second feature calculating means further includes means for outputting, as a set of said second features, an n-gram of a word of the causal relation expression specified by said causal relation specifying means, an n-gram of a sub-tree of the dependency tree in the causal relation, or a combination of a polarity of a predicate in said causal relation and meaning of a noun depending on the predicate, or any combination of these. 5. The non-factoid question-answering system according to claim 3, wherein said second feature calculating means further includes means for outputting, as a set of said second features, the number of words, sub-trees of dependency tree, pairs of a noun and polarities (excitatory/inhibitory) of predicates on which the noun depends on in a dependency tree, shared by said question and a result part of the causal relation expression specified by said causal relation specifying means, or any combination of these. 6. A non-factoid question-answering method, receiving a non-factoid question and generating an answer to the question, executed by using document storage means for storing a plurality of computer-readable documents, comprising: the candidate searching step, responsive to an input of a question, of taking out answer candidates to the question from said document storage means; the feature generating step, responsive to answer candidates searched at said candidate searching step, of generating, for combinations of said question and each of the answer candidates searched at said candidate searching step, a set of prescribed features; and the answer selecting step of calculating, when a set of said features generated at said feature generating step is given, for the question and the answer candidate as a base for generating the set of features, a score representing a degree of plausibility of the answer candidate as a correct answer to the question, and outputting an answer candidate having the highest score as an answer to the question; wherein said feature generating step includes the first feature calculating step of calculating, for said question and each answer candidate, a first set of features obtained from respective morpheme information and syntactic information, and the second feature calculating step of selecting a causal relation expression in each of said answer candidates relevant as giving an answer to said question, and of calculating a second set of features obtained from said causal relation expression; and said set of features include said first set of features and said second set of features.