A keyword selection strategy for dialogue move recognition and multi-class topic identification

The concept of usefulness for keyword selection in topic identification problems is reformulated and extended to the multi-class domain. The derivation is shown to be a generalisation of that for the two class problem. The technique is applied to both multinomial and Poisson based estimates of word probability, and shown to outperform or compare favourably to various information theoretic techniques classifying dialogue moves in the map task corpus, and reports in the LOB corpus.