Improved automatic keyword extraction given more linguistic knowledge
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Re-examining automatic keyphrase extraction approaches in scientific articles
MWE '09 Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications
Context-aware citation recommendation
Proceedings of the 19th international conference on World wide web
Automatic extraction and learning of keyphrases from scientific articles
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Journal of the American Society for Information Science and Technology
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Users of scientific papers databases, such as CiteSeer, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers. This is a well-studied problem given the complete text of the paper, but in many cases, due to copyright privileges, research papers databases do not have the complete text, only metadata, such as the title and abstract. On the other hand, research papers databases typically maintain the citation network of each paper. In this paper we study the problem of predicting which keywords are appropriate for a scientific paper, using only the citation network. We compare our method with predicting keywords using the title and abstract, concluding that the citation network provides much better predictions.