Dealing with the linguistic phenomena of spontaneous speech by the existing rule-based approach requires the preparation of complex analysis rules, which takes a great deal of effort. This paper describes a new method of extracting semantic information extraction from Japanese spontaneous speech by an example-based approach (EBA). Compared to the rule-based approach, EBA is robust and requires little effort for knowledge acquisition and its formation. In experimental evaluations of a semantic information extractor based on EBA, transcriptions of one hundred spontaneous dialogues are used as an example corpus and a testing corpus. The best performances of the extractor are 81.6% for precision rate and 62.2% for coverage rate in semantic feature extraction. The results suggest that our method is robust against unknown words and ill-formed sentences, and the extractor proved that EBA can be used as an effective tool for extracting semantic information from spontaneous speech.