Self-paced Compensatory Deep Boltzmann Machine for Semi-Structured Document Embedding

Self-paced Compensatory Deep Boltzmann Machine for Semi-Structured Document Embedding

Shuangyin Li, Rong Pan, Jun Yan

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2187-2193. https://doi.org/10.24963/ijcai.2017/304

In the last decade, there has been a huge amount of documents with different types of rich metadata information, which belongs to the Semi-Structured Documents (SSDs), appearing in many real applications. It is an interesting research work to model this type of text data following the way how humans understand text with informative metadata. In the paper, we introduce a Self-paced Compensatory Deep Boltzmann Machine (SCDBM) architecture that learns a deep neural network by using metadata information to learn deep structure layer-wisely for Semi-Structured Documents (SSDs) embedding in a self-paced way. Inspired by the way how humans understand text, the model defines a deep process of document vector extraction beyond the space of words by jointing the metadata where each layer selects different types of metadata. We present efficient learning and inference algorithms for the SCDBM model and empirically demonstrate that using the representation discovered by this model has better performance on semi-structured document classification and retrieval, and tag prediction comparing with state-of-the-art baselines.
Keywords:
Machine Learning: Machine Learning
Natural Language Processing: Information Extraction
Machine Learning: Deep Learning
Natural Language Processing: Natural Language Processing