History Integration into Semantic Classification

In spoken language systems, the classification of coherent linguistic/semantic phrases in terms of semantic classes is an important part of the whole understanding process. Basically, it relies on the plain text of the segment to be classified. Nevertheless, another important source of useful information is the dialogue context. In this paper, a number of different ways to integrate the dialogue history into the semantic classification are presented and tested on a corpus of person-to-person dialogues. Best result gives a 3.6% reduction of the error rate with respect to the performance obtained without using history.