1. A method for searching pieces of document data using a search keyword, the pieces of document data having a correlation with the search keyword or a relevant keyword associated with the search keyword and being a description of phenomena in a natural language, the method comprising: calculating, using a computer including a CPU, as a first vector, respective first scores at which or respective probabilities that each of the pieces of document data belongs to clusters or classes for clustering or classifying pieces of document data; calculating, upon an entry of a search keyword, as a second vector, respective second scores at which or respective probabilities that the search keyword or a relevant keyword associated with the search keyword belongs to the clusters or the classes; calculating an inner product of each of the first vectors and the second vector, the calculated inner product being a third score of the corresponding piece of document data regarding the search keyword; and acquiring a correlation value from document data containing each keyword in a classification keyword set and document data with the third score that is equal to or more than a predetermined threshold or is included in a predetermined high-ranking proportion, wherein the correlation value is acquired according to a correlation function corr concept (s,t) described below: Equation ⁢ ⁢ 1 corr concept ⁡ ( s , t ) = P concept ⁡ ( s ⋂ t ) P concept ⁡ ( s ) ⁢ P concept ⁡ ( t ) [ E ⁢ ⁢ 1 ] Equation ⁢ ⁢ 2 P concept ⁡ ( s ) = 1 N ⁢ ∑ d = documents ⁢ ⁢ 〈 s ❘ d 〉 [ E ⁢ ⁢ 2 ] Equation ⁢ ⁢ 3 P concept ⁡ ( t ) = 1 N ⁢ ∑ d = documents ⁢ ⁢ 〈 t ❘ d 〉 [ E ⁢ ⁢ 3 ] Equation ⁢ ⁢ 4 P concept ⁡ ( s ⋂ t ) = 1 N ⁢ ∑ d = documents ⁢ ⁢ 〈 s ❘ d 〉 ⁢ 〈 t ❘ d 〉 [ E ⁢ ⁢ 4 ] where s is a facet keyword, t is a search keyword, d is document data, N is a total number of pieces of document data, <s|d> is an inner product between the document data (d) and the facet keyword (s), <t|d> is an inner product between the document data (d) and the search keyword (t), P concept (s) is a probability that a word conceptually matching the facet keyword s occurs in the document data, and P concept (t) is a probability that a word conceptually matching the search keyword occurs in the document data.