A Graph-Based Approach to Topic Clustering for Online Comments to News View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2016

AUTHORS

Ahmet Aker , Emina Kurtic , A. R. Balamurali , Monica Paramita , Emma Barker , Mark Hepple , Rob Gaizauskas

ABSTRACT

This paper investigates graph-based approaches to labeled topic clustering of reader comments in online news. For graph-based clustering we propose a linear regression model of similarity between the graph nodes (comments) based on similarity features and weights trained using automatically derived training data. To label the clusters our graph-based approach makes use of DBPedia to abstract topics extracted from the clusters. We evaluate the clustering approach against gold standard data created by human annotators and compare its results against LDA – currently reported as the best method for the news comment clustering task. Evaluation of cluster labelling is set up as a retrieval task, where human annotators are asked to identify the best cluster given a cluster label. Our clustering approach significantly outperforms the LDA baseline and our evaluation of abstract cluster labels shows that graph-based approaches are a promising method of creating labeled clusters of news comments, although we still find cases where the automatically generated abstractive labels are insufficient to allow humans to correctly associate a label with its cluster. More... »

PAGES

15-29

References to SciGraph publications

Book

TITLE

Advances in Information Retrieval

ISBN

978-3-319-30670-4
978-3-319-30671-1

Author Affiliations

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-30671-1_2

DOI

http://dx.doi.org/10.1007/978-3-319-30671-1_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043830439


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