This paper proposes an error-tolerant question answering method for spoken documents. Though the question answering system for written documents can be directly applied to the transcribed spoken documents by using a LVCSR system, the recognition errors significantly degrade the QA performance. Especially, it is often the case that the answer itself is missrecognized and in that case it becomes quite difficult to find the answer. To cope with such a problem, instead of conventional NE

more »

... the proposed method utilizes named entity detection that decides only whether a section of speech, i.e. an utterance, contains named entities of a specific type. Because the NE detection is much easier task and utilized wider context than the NE extraction, it is expected to work robustly for erroneous transcribed speech data. The experimental results showed that the proposed method outperformed the baseline methods with respect to the spoken document with recognition errors.