Bayesian Multiple Instance Learning with Input from Attention Mechanism for Text Analysis
Speaker: Professor Jing Cao (Southern Methodist University)
Zoom link: https://uni-sydney.zoom.us/j/83582933974
As a branch of machine learning, multiple instance learning (MIL) learns from a collection of labeled bags, each containing a set of instances. Each instance is described by a feature vector. Since its emergence, MIL has been applied to solve various problems including content-based image retrieval, object tracking/detection, and computer-aided diagnosis. In this study, we apply MIL to text sentiment analysis. The current neural-network-based approaches in text analysis enjoy high classification accuracies but usually lack interpretability. The proposed Bayesian MIL model treats each text document as a bag, where the words are the instances. The model has a two-layered structure. The first layer identifies whether a word is essential or not (i.e., primary instance), and the second layer assigns a sentiment score over the individual words of a document. The motivation of our approach is that by the combination of the attention mechanism from neural networks with a relatively simple statistical model, hopefully, we can combine the best of two worlds: the interpretability of a statistical model and the high predictive performance of neural-network models.
As a branch of machine learning, multiple instance learning (MIL) learns from a collection of labeled bags, each containing a set of instances. Each instance is described by a feature vector. Since its emergence, MIL has been applied to solve various problems including content-based image retrieval, object tracking/detection, and computer-aided diagnosis. In this study, we apply MIL to text sentiment analysis. The current neural-network-based approaches in text analysis enjoy high classification accuracies but usually lack interpretability. The proposed Bayesian MIL model treats each text document as a bag, where the words are the instances. The model has a two-layered structure. The first layer identifies whether a word is essential or not (i.e., primary instance), and the second layer assigns a sentiment score over the individual words of a document. The motivation of our approach is that by the combination of the attention mechanism from neural networks with a relatively simple statistical model, hopefully, we can combine the best of two worlds: the interpretability of a statistical model and the high predictive performance of neural-network models.