摘要：The main goal of this paper is to explore the predictive
power
of dialogue context
on Dialogue
Act classification, both as concerns the linear context provided by previous dialogue acts, and
the hierarchical context specified by conversational games. As our learning approach, we extend
Latent Semantic Analysis (LSA) as Feature LSA (FLSA), and combine FLSA with the k-Ncarest
Neighbor algorithm. FLSA adds richer linguistic features to LSA, which only uses words. The
k-Nearest Neighbor algorithm obtains its best results when applied to the reduced semantic spaces
generated by FLSA. Empirically, our results are better than previously published results on two
different corpora, MapTask and CallHome Spanish. Linguistically, we confirm and extend previous
observations that the hierarchical dialogue structure encoded via the notion of game is of primary
importance for dialogue act recognition.
关键词：Dialogue Acts; I atent Semantic Analysis; k-Nearest Neighbor; Dialogue ( james
