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

Term weighting schemes are widely used in Natural Language Processing andInformation Retrieval. In particular, term weighting is the basis for keywordextraction. However, there are relatively few evaluation studies that shedlight about the strengths and shortcomings of each weighting scheme. In fact,in most cases researchers and practitioners resort to the well-known tf-idf asdefault, despite the existence of other suitable alternatives, includinggraph-based models. In this paper, we perform an exhaustive and large-scaleempirical comparison of both statistical and graph-based term weighting methodsin the context of keyword extraction. Our analysis reveals some interestingfindings such as the advantages of the less-known lexical specificity withrespect to tf-idf, or the qualitative differences between statistical andgraph-based methods. Finally, based on our findings we discuss and devise somesuggestions for practitioners. Source code to reproduce our experimentalresults, including a keyword extraction library, are available in the followingrepository: https://github.com/asahi417/kex

Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction