10. A non-transitory computer readable medium storing instructions executable by a processor, the instructions, when executed, causing the processor to perform a method for determining similarity between two input documents, the method comprising: detecting k terms that occur in each of said input documents; segmenting each of said input documents into document segments, each segment being a predetermined part of said input document; generating document segment vectors, each vector including as its element values according to occurrence frequencies of said terms occurring in said respective document segments, where a n-th document segment vector for a first one of said input documents S n (n=1, . . . , N) is represented by (s n1 , s n2 , s n3 , . . . , s nk ) and a m-th document segment vector for a second one of said input documents T m (m=1, . . . , M) is represented by (t m1 , t m2 , t m3 , . . . , t mk ), where s ni , represents the occurrence frequency of an i-th term among a total of k terms in a n-th document segment of said first input document, and t mi represents the occurrence frequency of the i-th term in a m-th document segment of said second input document; calculating eigenvalues and eigenvectors of square sum matrices of document segment vectors for said two input documents, where the square sum matrix B of the first input document is a k×k matrix, k>1, and each component B ab of said square sum matrix B indicates a degree of co-occurrence of a-th and b-th terms (a, b=1, . . . , k) in said first input document and is calculated by 
 B ab =E n=1 N S na S nb , and the square sum matrix C of the second input document is a k×k matrix, k 1, and each component C ab of said square sum matrix C indicates a degree of co-occurrence of a-th and b-th terms (a, b=1, . . . , k) in said second input document and is calculated by 
 C ab =Σ m=1 M T ma T mb ; selecting, as the basis vectors, eigenvectors corresponding to the larger eigenvalues from said calculated eigenvectors; calculating weighted sum of squared inner products between said basis vectors of one of said two input documents and document segment vectors of the other document of said two input documents and summing the squared inner products with weights; and determining the similarity between said two input documents based on said weighted sum of squared inner products.
10. A non-transitory computer readable medium storing instructions executable by a processor, the instructions, when executed, causing the processor to perform a method for determining similarity between two input documents, the method comprising: detecting k terms that occur in each of said input documents; segmenting each of said input documents into document segments, each segment being a predetermined part of said input document; generating document segment vectors, each vector including as its element values according to occurrence frequencies of said terms occurring in said respective document segments, where a n-th document segment vector for a first one of said input documents S n (n=1, . . . , N) is represented by (s n1 , s n2 , s n3 , . . . , s nk ) and a m-th document segment vector for a second one of said input documents T m (m=1, . . . , M) is represented by (t m1 , t m2 , t m3 , . . . , t mk ), where s ni , represents the occurrence frequency of an i-th term among a total of k terms in a n-th document segment of said first input document, and t mi represents the occurrence frequency of the i-th term in a m-th document segment of said second input document; calculating eigenvalues and eigenvectors of square sum matrices of document segment vectors for said two input documents, where the square sum matrix B of the first input document is a k×k matrix, k>1, and each component B ab of said square sum matrix B indicates a degree of co-occurrence of a-th and b-th terms (a, b=1, . . . , k) in said first input document and is calculated by 
 B ab =E n=1 N S na S nb , and the square sum matrix C of the second input document is a k×k matrix, k 1, and each component C ab of said square sum matrix C indicates a degree of co-occurrence of a-th and b-th terms (a, b=1, . . . , k) in said second input document and is calculated by 
 C ab =Σ m=1 M T ma T mb ; selecting, as the basis vectors, eigenvectors corresponding to the larger eigenvalues from said calculated eigenvectors; calculating weighted sum of squared inner products between said basis vectors of one of said two input documents and document segment vectors of the other document of said two input documents and summing the squared inner products with weights; and determining the similarity between said two input documents based on said weighted sum of squared inner products. 11. The non-transitory computer readable medium of claim 10 , further comprising, prior to segmenting each of said input documents into document segments, performing a morphological analysis for the detected terms, including assigning a part of speech to each of the detected terms.