A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Improving browsing in digital libraries with keyphrase indexes
Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
A probabilistic model of information retrieval: development and comparative experiments Part 2
Information Processing and Management: an International Journal
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Algorithms for Keyphrase Extraction
Information Retrieval
Using Noun Phrase Heads to Extract Document Keyphrases
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
A framework for selective query expansion
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Improved automatic keyword extraction given more linguistic knowledge
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
On ranking the effectiveness of searches
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking robustness: a novel framework to predict query performance
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Information Systems
Using lexical chains for keyword extraction
Information Processing and Management: an International Journal
Relevance models for topic detection and tracking
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Introduction to Information Retrieval
Introduction to Information Retrieval
Clustering Narrow-Domain Short Texts by Using the Kullback-Leibler Distance
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Coherent keyphrase extraction via web mining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Exploiting neighborhood knowledge for single document summarization and keyphrase extraction
ACM Transactions on Information Systems (TOIS)
Using Kullback-Leibler distance for text categorization
ECIR'03 Proceedings of the 25th European conference on IR research
Topical keyphrase extraction from Twitter
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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Previous research shows that keyphrases are useful tools in document retrieval and navigation. While these point to a relation between keyphrases and document retrieval performance, no other work uses this relationship to identify keyphrases of a given document. This work aims to establish a link between the problems of query performance prediction (QPP) and keyphrase extraction. To this end, features used in QPP are evaluated in keyphrase extraction using a na脙炉ve Bayes classifier. Our experiments indicate that these features improve the effectiveness of keyphrase extraction in documents of different length. More importantly, commonly used features of frequency and first position in text perform poorly on shorter documents, whereas QPP features are more robust and achieve better results.