Deep Structured Semantic Model (DSSM) Algorithm
- See: Deep Learning Neural Network, Deep Learning System Semantic Analysis, Vectorial Semantics, Artificial Neural Network, Synonymy, Polysemy, Word Hashing, Neural Natural Language Processing System.
- (Huang et al., 2013) ⇒ Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. (2013). “Learning Deep Structured Semantic Models for Web Search Using Clickthrough Data.” In: Proceedings of the 22nd ACM International Conference on Conference on information & knowledge management. ISBN:978-1-4503-2263-8 doi:10.1145/2505515.2505665
- QUOTE: The typical DNN architecture we have developed for mapping the raw text features into the features in a semantic space is shown in Fig. 1. The input (raw text features) to the DNN is a highdimensional term vector, e.g., raw counts of terms in a query or a document without normalization, and the output of the DNN is a concept vector in a low-dimensional semantic feature space. Illustration of the DSSM. It uses a DNN to map high-dimensional sparse text features into low-dimensional dense features in a semantic space. The first hidden layer, with 30k units, accomplishes word hashing. The word-hashed features are then projected through multiple layers of non-linear projections. The final layer’s neural activities in this DNN form the feature in the semantic space.