基於深層類神經網路及表示學習技術之文件可讀性分類(Classification of Text Readability Based on Deep Neural Network and Representation Learning Techniques)[In Chinese]

The development of the internet has facilitated the flow of information. However, this explosive growth of information has led to fundamental importance being overlooked: Reading material can be understood. Research on readability formulas aims to predict, to a reasonable extent, the degree to which a text can be understood. It does so mainly by analyzing and translating the information within a text into readability features, which are used to train a readability model, in order to automatically predict the readability of a given text. In recent years, the development of deep neural networks, applied to speech recognition, image processing and natural language processing has improved significantly on the performance. Therefore, this paper proposes a readability model built with deep neural network and word vector representation, and which is capable of analyzing cross-domain texts, in accordance with the diverse topics of text contents. The authors aim to make the readability model capable of analyzing text readability with more accurate, as well as possess domain generalization capacity. 關鍵詞:可讀性,詞向量,分類,深層類神經網路,支向量機