Graph-Based Semantic Learning, Representation and Growth from Text: A Systematic Review

摘要

The Vector Space Model (VSM), is the main technique to model the semantics from the text. However, the VSM model suffers from notable limitations. The main alternative model for VSM model is a graph-based model. This paper presents a systematic review on the graph-based processes of Semantic Learning, Representing and Growth (SLRG) from the text. Then it describes a new branch in graph-based SLRG modeling, inspired from the cognitive-semantics.