1. A non-transitory computer readable medium storing a program causing a computer to execute a process for classifying multilingual documents, the process comprising: extracting, concerning first document information including a plurality of supervised texts of a first language, a word sense associated with a word included in each of the plurality of supervised texts in the first document information from predetermined word-sense information; setting an extracted word sense to be a teacher signal for each of the plurality of supervised texts included in the first document information; creating a first topic model by executing supervised topic modeling on the first document information by using the set teacher signal for each of the plurality of supervised texts included in the first document information; estimating a topic of each of the plurality of supervised texts included in the first document information by using the created first topic model; generating a learning model by executing supervised machine learning by using, as a feature, the estimated topic of each of the plurality of supervised texts included in the first document information and by using, as a category, the teacher signal for each of the plurality of supervised texts in the first document information; extracting, concerning second document information including a plurality of supervised texts of a second language and concerning a field identical to a field of the first document information, a word sense associated with a word included in each of the plurality of supervised texts in the second document information from the predetermined word-sense information; setting an extracted word sense to be a teacher signal for each of the plurality of supervised texts included in the second document information; creating a second topic model by executing supervised topic modeling on the second document information by using the set teacher signal for each of the plurality of supervised texts included in the second document information; estimating a topic of each of the plurality of supervised texts included in the second document information by using the created second topic model; and estimating a category of each of the plurality of supervised texts included in the second document information by using, as a feature, the estimated topic of an associated supervised text and by using the generated learning model.