W-rank: A keyphrase extraction method for webpage based on linguistics and DOM-base features | VAWKUM Transactions on Computer Sciences 2023

Author(s)

Himat Shah School of Computing, University of Eastern Finland,Joensuu,Finland

Shafique Ahmed Awan Department of CS& IT, Benazir Bhutto Shaheed University,Karachi,Sindh,Pakistan

Anwar Ali Sathio Department of CS& IT, Benazir Bhutto Shaheed University,Karachi,Sindh,Pakistan

Asadullah Burdi Institute of Mathematics and Computer Science (IMCS), University of Sindh Jamshoro,Sindh,Pakistan

Abstract

This paper addresses the problem of an automatic keyphrase extraction for a webpage text. Our method is unsupervised, and we call it W-rank. In our method, first we extract the text of a webpage and tokenize into three different candidate words list: unigram ,bigrams and noun phrases. Then we assign score to all words based on their individual appearance in linguistic and DOM-based feature sets. In the final step, we rank these candidate words using score and select top 5 keyphrase from each list and combine them as a final keyphrases for a given webpage. We focus more on the relevancy of keyphrases to its content using linguistic features. We compare our method with other methods using precision, recall and f-score. The experimental result shows, W-rank improves the performance of our previous method D-rank and outperforms other state of art methods.

Publication Details

Page(s) 217-228

DOI DOI not available

Published Journal: VAWKUM Transactions on Computer Sciences, Volume: 11, Issue: 1, Year: 2023

Keywords

Scoring Wrank Automatic keyphrase extraction relevancy webpage text unsupervised method

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