Automatic label generation for news comment clusters
| Autor: | Emma Barker, Emina Kurtic, Mark Hepple, Monica Lestari Paramita, Ahmet Aker, Robert Gaizauskas, Adam Funk |
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| Rok vydání: | 2016 |
| Předmět: |
Information retrieval
Computer science Pie chart 02 engineering and technology 010501 environmental sciences 01 natural sciences law.invention Task (project management) ComputingMethodologies_PATTERNRECOGNITION law 0202 electrical engineering electronic engineering information engineering Cluster (physics) 020201 artificial intelligence & image processing Baseline (configuration management) Feature set 0105 earth and related environmental sciences |
| Zdroj: | INLG Scopus-Elsevier |
| Popis: | We present a supervised approach to automat- ically labelling topic clusters of reader com- ments to online news. We use a feature set that includes both features capturing proper- ties local to the cluster and features that cap- ture aspects from the news article and from comments outside the cluster. We evaluate the approach in an automatic and a manual, task-based setting. Both evaluations show the approach to outperform a baseline method, which uses tf*idf to select comment-internal terms for use as topic labels. We illustrate how cluster labels can be used to generate cluster summaries and present two alternative sum- mary formats: a pie chart summary and an ab- stractive summary. |
| Databáze: | OpenAIRE |
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