其他摘要：In statistical machine learning approaches for question classification, efforts based on lexical feature space require high computation power and complex data structures. This is due to the large number of unique words (or high dimensionality). Choosing semantic features instead could significantly reduce the dimensionality of the feature space. This article describes the use of Extreme Learning Machine (ELM) for question classification based on semantic features to improve both the training and testing speeds compared to the benchmark Support Vector Machine (SVM) classifier. Improvements have also been made to the head word extraction and word sense disambiguation processes. These have resulted in a higher accuracy (an increase of 0.2%) for the classification of coarse classes compared to the benchmark. For the fine classes, however, there is a 1.0% decrease in accuracy but is compensated by a significant increase in speed (92.1% on average).
