other,6-1-J86-1002,ak for <term> error correction </term> of <term> ill-formed input </term> is described that acquires <term> dialogue
model,12-1-J86-1002,ak input </term> is described that acquires <term> dialogue patterns </term> in typical usage and uses these <term>
model,20-1-J86-1002,ak </term> in typical usage and uses these <term> patterns </term> to predict new <term> inputs </term>
other,24-1-J86-1002,ak <term> patterns </term> to predict new <term> inputs </term> . <term> Error correction </term> is
tech,7-2-J86-1002,ak </term> is done by strongly biasing <term> parsing </term> toward <term> expected meanings </term>
other,9-2-J86-1002,ak biasing <term> parsing </term> toward <term> expected meanings </term> unless clear evidence from the <term>
other,16-2-J86-1002,ak </term> unless clear evidence from the <term> input </term> shows the current <term> sentence </term>
other,20-2-J86-1002,ak <term> input </term> shows the current <term> sentence </term> is not expected . A <term> dialogue
tech,1-3-J86-1002,ak sentence </term> is not expected . A <term> dialogue acquisition and tracking algorithm </term> is presented along with a description
tech,17-3-J86-1002,ak description of its implementation in a <term> voice interactive system </term> . A series of tests are described
tech,12-4-J86-1002,ak described that show the power of the <term> error correction methodology </term> when stereotypic <term> dialogue </term>
other,17-4-J86-1002,ak methodology </term> when stereotypic <term> dialogue </term> occurs . In this paper we explore
hide detail