Spontaneous speech and natural language processing. ALPES: a robust semantic-led parser

The need for robust parsers is becoming more and more essential as spoken human machine communication is developed. Because of its uncontrolled nature, spontaneous speech presents a high rate of extagrammatical constructions (hesitations, repetitions, self corrections, etc.). As a result, spontaneous speech rapidly catches out most syntactic parsers, in spite of the frequent addition of some corrective methods (S. Seneff, 1992). Therefore, most dialog systems restrict the linguistic analysis of the spoken utterances to a simple extraction of keywords (D. Appelt and E. Jakson, 1992). This selective approach led to significant results in some restricted applications (ATIS), but it does not seem appropriate for higher level tasks, for which the utterances cannot be reduced to a simple set of keywords. As a result, neither the syntactic methods nor the selective approaches can fully satisfy the constraints of robustness and exhaustibility required by the human machine communication. The paper precisely presents a detailed semantic parser (ALPES) which masters most spoken utterances.