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This paper proposes a bootstrapping method of constructing statistical language models for new spoken dialogue systems by collecting and selecting sentences from the World Wide Web (WWW). To make effective search queries that cover the target domain in full detail, we exploit the document set described about the target domain as seeding data. An important issue is how to filter the retrieved Web pages, since all of the retrieved Web texts are not necessarily suitable as training data. We induct an existing dialogue corpus of different domain to prefer the texts of spoken style. The proposed method was evaluated on two different tasks of software support and sightseeing guidance, and significant reduction of the word error rate was achieved. We show that it is vital to incorporate the dialogue corpus, though not relevant to the target domain, in the text selection phase.