English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Keyphrases are very useful and significant for information retrieval, automatic summarizing, text clustering, etc. KEA is a traditional and classical algorithm in keyphrase automatic extraction. But it is mainly based on the statistical information without considering the semantic information. In this paper, We propose a method which combine semantic information with KEA by constructing lexical chain that based on Reget's thesaurus. In our method, the semantic similarity between terms is used to construct the lexical chain, and then we use the length of the chain as a feature to build the extraction model. The experiment result shows that the performance of the system has a big improvement compare with the KEA. Keywords-keyphrases extraction; KEA; lexcial chain;semantic similarity.

Improvement of KEA Based on Lexical Chain