| dc.description.abstract | An unprecedented way is accomplished by using concept words derived from statistical context analysis between sentences which is better than traditional methods that use only keyword representation. Through scaling to a very large dataset we proposed an algorithm which discovers, and describes events with effective keyword networks, based on their coexisting peripheral co-occurrences. In our experiment, we used real-world news, and supervised them into paraphrases by weighting for the all attempted events. We evaluated our scheme by a set of terms that maximally discriminated the percussion in news and which also keep the evidences. Here we are classifying the events with a multilayer perceptron by executing auto-convolution methodology in back propagation. | en_US |
