1. A method for generating, with a processor, semantic information associated with spoken voice in order to control an electronic device, comprising: acquiring, with a processor, a first text corpus, including first text data of a first sentence including a first word and described in a natural language, and second text data of a second sentence including a second word different in meaning from the first word, with a second word distribution indicating types and frequencies of words appearing within a predetermined range prior to and subsequent to the second word being similar to a first word distribution within the predetermined range prior to and subsequent to the first word in the first sentence; acquiring, with the processor, a second text corpus including third text data of a third sentence, including a third word identical to at least one of the first word and the second word, with a third word distribution within the predetermined range prior to and subsequent to the third word being not similar to the first word distribution; in accordance with an arrangement of a word string in the first text corpus and the second text corpus, performing, with the processor, a learning process by assigning to the first word a first vector representing a meaning of the first word in a vector space of predetermined dimensions and by assigning to the second word a second vector representing a meaning of the second word in the vector space; storing the first vector in association with the first word, and the second vector spaced by a predetermined distance or longer from the first vector in the vector space in association with the second word; and generating, with the processor, a command based on the first vector and the second vector, wherein the electronics device is controlled in accordance with the command.
1. A method for generating, with a processor, semantic information associated with spoken voice in order to control an electronic device, comprising: acquiring, with a processor, a first text corpus, including first text data of a first sentence including a first word and described in a natural language, and second text data of a second sentence including a second word different in meaning from the first word, with a second word distribution indicating types and frequencies of words appearing within a predetermined range prior to and subsequent to the second word being similar to a first word distribution within the predetermined range prior to and subsequent to the first word in the first sentence; acquiring, with the processor, a second text corpus including third text data of a third sentence, including a third word identical to at least one of the first word and the second word, with a third word distribution within the predetermined range prior to and subsequent to the third word being not similar to the first word distribution; in accordance with an arrangement of a word string in the first text corpus and the second text corpus, performing, with the processor, a learning process by assigning to the first word a first vector representing a meaning of the first word in a vector space of predetermined dimensions and by assigning to the second word a second vector representing a meaning of the second word in the vector space; storing the first vector in association with the first word, and the second vector spaced by a predetermined distance or longer from the first vector in the vector space in association with the second word; and generating, with the processor, a command based on the first vector and the second vector, wherein the electronics device is controlled in accordance with the command. 5. The method according to claim 1 , wherein the second word is similar in meaning to the first word, but different in terms of a degree of similarity from the first word.