Learning to Extract Comparison Points of Entity Pairs from Wikipedia Articles
Authors | Sandeep Kumar Pani R Naresh Pawan Goyal Plaban Kumar Bhowmick |
|---|---|
Publication date | 2018 |
DOI | 10.1145/3197026.3203909 |
Links | Original |
Learning to Extract Comparison Points of Entity Pairs from Wikipedia Articles - scientific work related to Wikipedia quality published in 2018, written by Sandeep Kumar Pani, R Naresh, Pawan Goyal and Plaban Kumar Bhowmick.
Overview
In this paper, authors present preliminary results on a novel task of extracting comparison points for a pair of entities from the text articles describing them. The task is challenging as comparison points in a typical pair of articles tend to be sparse. Authors presented a multi-level document analysis (viz. document, paragraph and sentence level) for extracting the comparisons. For extracting sentence level comparisons, which is the hardest task among three, authors have used Convolutional Neural Network (CNN) with features extracted around triple. Experiments conducted on a small dataset provide encouraging performance.
Embed
Wikipedia Quality
Pani, Sandeep Kumar; Naresh, R; Goyal, Pawan; Bhowmick, Plaban Kumar. (2018). "[[Learning to Extract Comparison Points of Entity Pairs from Wikipedia Articles]]". ACM Press. DOI: 10.1145/3197026.3203909.
English Wikipedia
{{cite journal |last1=Pani |first1=Sandeep Kumar |last2=Naresh |first2=R |last3=Goyal |first3=Pawan |last4=Bhowmick |first4=Plaban Kumar |title=Learning to Extract Comparison Points of Entity Pairs from Wikipedia Articles |date=2018 |doi=10.1145/3197026.3203909 |url=https://wikipediaquality.com/wiki/Learning_to_Extract_Comparison_Points_of_Entity_Pairs_from_Wikipedia_Articles |journal=ACM Press}}
HTML
Pani, Sandeep Kumar; Naresh, R; Goyal, Pawan; Bhowmick, Plaban Kumar. (2018). "<a href="https://wikipediaquality.com/wiki/Learning_to_Extract_Comparison_Points_of_Entity_Pairs_from_Wikipedia_Articles">Learning to Extract Comparison Points of Entity Pairs from Wikipedia Articles</a>". ACM Press. DOI: 10.1145/3197026.3203909.
