A Set Space Model for Feature Calculus

Processing natural language at the sentence level suffers from a sparse-feature problem caused by the limited number of words in a sentence. In this article, a Set Space Model (SSM) is proposed to utilize sentence information, the main idea being that, depending on structural characteristics or functional principles of linguistics, features in a sentence can be grouped into different sets. Feature calculus can then operate on the grouped features and capture structural information using external knowledge. The authors implement this method in a traditional information extraction task, with results showing significant and constant improvement in general information extraction.

Authors

• Yanping Chen Guizhou University
• Qinghua Zheng School of Computer Science and Technology, Xi'an Jiaotong University, China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering, Xi'an Jiaotong University, China
• Ping Chen

Keywords

• Feature extraction
• Calculus
• Semantics
• Pragmatics
• Set theory
• Information retrieval
• Artificial intelligence
• Natural language processing
• Sparse matrices
• High-dimensional
• Structural Information
• Natural Language
• Combination Of Features
• Information Extraction
• Word Embedding
• Words In Sentences
• External Knowledge
• Sentence Level
• Sparse Feature
• Linguistic Units
• Noisy Features
• Sentence Information
• Information Retrieval Systems
• Knowledge Base
• Feature Space
• Morphemes
• Postage
• Measure Space
• Negative Instances
• Atomic Features
• Relation Extraction
• Positive Instances
• Latent Dirichlet Allocation
• Term Frequency-inverse Document Frequency
• WordNet
• Possible Worlds
• Space Transformation
• Named Entity Recognition
• Set Space Model
• feature calculus
• intelligent systems