Background:
As a known key phrase extraction algorithm, TextRank is an analogue of PageRank algorithm,
which relied heavily on the statistics of term frequency in the manner of co-occurrence analysis.
Objective:
The frequency-based characteristic made it a neck-bottle for performance enhancement, and various improved
TextRank algorithms were proposed in the recent years. Most of improvements incorporated semantic information into key
phrase extraction algorithm and achieved improvement.
Method:
In this research, taking both syntactic and semantic information into consideration, we integrated syntactic tree
algorithm and word embedding and put forward an algorithm of Word Embedding and Syntactic Information Algorithm
(WESIA), which improved the accuracy of the TextRank algorithm.
Results:
By applying our method on a self-made test set and a public test set, the result implied that the proposed unsupervised key phrase extraction algorithm outperformed the other algorithms to some extent.
Fast Extraction of Word Embedding from Q-contexts
