NDLI: Unsupervised graph-based topic labelling using dbpedia

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Unsupervised graph-based topic labelling using dbpedia

Content Provider	ACM Digital Library
Author	Greene, Derek Hulpus, Ioana Hayes, Conor Karnstedt, Marcel
Abstract	Automated topic labelling brings benefits for users aiming at analysing and understanding document collections, as well as for search engines targetting at the linkage between groups of words and their inherent topics. Current approaches to achieve this suffer in quality, but we argue their performances might be improved by setting the focus on the structure in the data. Building upon research for concept disambiguation and linking to DBpedia, we are taking a novel approach to topic labelling by making use of structured data exposed by DBpedia. We start from the hypothesis that words co-occuring in text likely refer to concepts that belong closely together in the DBpedia graph. Using graph centrality measures, we show that we are able to identify the concepts that best represent the topics. We comparatively evaluate our graph-based approach and the standard text-based approach, on topics extracted from three corpora, based on results gathered in a crowd-sourcing experiment. Our research shows that graph-based analysis of DBpedia can achieve better results for topic labelling in terms of both precision and topic coverage.
Starting Page	465
Ending Page	474
Page Count	10
File Format	PDF
ISBN	9781450318693
DOI	10.1145/2433396.2433454
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-02-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Topic labelling Graph centrality measures Dbpedia Latent dirichlet allocation
Content Type	Text
Resource Type	Article

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