Abstract: 【Purpose/significance】This paper investigates the effectiveness of classification features and compares the performance of
three classifiers in a query ambiguity intent classification task.【Method/process】This paper first constructs a query taxonomy of ambi? guity and then extracts query-based features and document-based features.Later,it tests accuracy,using decision tree,neural network, k-nearest neighbor individually,with various combinations of features.【Result/conclusion】①An accuracy is increased by 49.5% com? pared with the baseline; ②Compared with document-based features,using query-based features achieves better accuracy; ③Decision tree performs best among the tested classifiers.【Innovation/limitation】A query taxonomy of ambiguity is constructed; A query classifi? cation task based on three types of ambiguity is realized; Due to dataset accessibility,our experiments are done on a limited size datas? et.