Automatic detection of semantic boundaries
Mauro Cettolo, Anna Corazza · 1997
In spoken language systems, the segmentation of utterances into coherent linguistic/semantic units is very useful, as it makes easier processing after the speech recognition phase. In this paper, a methodology for semantic boundary prediction is presented and tested on a corpus of person-to-person dialogues. The approach is based on binary decision trees and uses text context, including broad classes of silent pauses, filled pauses and human noises. Best results give more than 90% precision, almost 80% recall and about 3% false alarms. 1. INTRODUCTION This work focuses on the automatic segmentation of dialogue turns into homogeneous Semantic Units (SUs) [7]. The approach described below is evaluated in the domain of appointment scheduling, where a system able to deal with this kind of interaction between two persons speaking different languages is being developed [1]. As a working hypothesis, it is assumed that each turn can be represented as a "flat" sequence of concepts, i.e. no nes...