KEA: practical automatic keyphrase extraction
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Learning Algorithms for Keyphrase Extraction
Information Retrieval
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TSD '99 Proceedings of the Second International Workshop on Text, Speech and Dialogue
Discovery of Frequent Word Sequences in Text
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ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
Keyphrase Extraction Using Semantic Networks Structure Analysis
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A ranking approach to keyphrase extraction
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Single document keyphrase extraction using neighborhood knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Tool for computer-aided Spanish word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
SemEval-2010 task 5: Automatic keyphrase extraction from scientific articles
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
BUAP: An unsupervised approach to automatic keyphrase extraction from scientific articles
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Conundrums in unsupervised keyphrase extraction: making sense of the state-of-the-art
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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This paper presents an unsupervised graph-based method to extract keyphrases using semantic information. The proposed method has two stages. In the first one, we have extracted MFS (Maximal Frequent Sequences) and built the nodes of a graph with them. The weight of the connection between two nodes has been established according to common statistical information and semantic relatedness. In the second stage, we have ranked MFS with traditionally PageRank algorithm; but we have included ConceptNet. This external resource adds an extra weight value between two MFS. The experimental results are competitive with traditional approaches developed in this area. MFSRank overcomes the baseline for top 5 keyphrases in precision, recall and F-score measures.