專題演講 主講人：羅小華教授（哥倫比亞大學統計系教授、陽明交大統計所講座教授）
題 目：Language Semantics Interpretation with an Interaction-Based Recurrent Neural Network
主講人：羅小華教授（哥倫比亞大學統計系教授、陽明交大統計所講座教授）
時 間：112年11月30日(星期四)下午15:30-16:20
(15:10-15:30茶會於綜合一館428室舉行)
地 點：綜合一館A427
摘要
Text classification is a fundamental language task in Natural Language Processing. A variety of sequential models are capable of making good predictions, yet there is a lack of connection between language semantics and prediction results. This paper proposes a novel influence score (I-score), a greedy search algorithm, called Backward Dropping Algorithm (BDA), and a novel feature engineering technique called the “dagger technique”. First, the paper proposes to use the novel influence score (I-score) to detect and search for the important language semantics in text documents that are useful for making good predictions in text classification tasks. Next, a greedy search algorithm, called the Backward Dropping Algorithm, is proposed to handle long-term dependencies in the dataset. Moreover, the paper proposes a novel engineering technique called the “dagger technique” that fully preserves the relationship between the explanatory variable and the response variable. The proposed techniques can be further generalized into any feed-forward Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), and any neural network. A real-world application on the Internet Movie Database (IMDB) is used and the proposed methods are applied to improve prediction performance with an 81% error reduction compared to other popular peers if I-score and “dagger technique” are not implemented.
Keywords: neural networks; interaction-based learning; I-score; dagger technique