Relation Extraction for Massive News Texts

With the development of information technology including Internet technologies, the amount of textual information that people need to process daily is increasing. In order to automatically obtain valuable and user-informed information from massive amounts of textual data, many researchers have conducted in-depth research in the area of entity relation extraction. Based on the existing research of word vector and the method of entity relation extraction, this paper designs and implements an method based on support vector machine (SVM) for extracting English entity relationships from massive news texts. The method converts sentences in natural language into a form of numerical matrix that can be understood and processed by computers through word embedding and position embedding. Then the key features are extracted, and feature vectors are constructed and sent to the SVM classifiers for relation classification. In the process of feature extraction, we had two different models to finish the job, one by Principal Component Analysis (PCA) and the other by Convolutional Neural Networks (CNN). We designed experiments to evaluate the algorithm.