Transition network grammars for natural language analysis
Communications of the ACM
Learning Algorithms for Keyphrase Extraction
Information Retrieval
Augmented Transition Networks as a design tool for personalized database systems
SIGIR '78 Proceedings of the 1st annual international ACM SIGIR conference on Information storage and retrieval
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
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A Supervised Framework for Keyword Extraction From Meeting Transcripts
IEEE Transactions on Audio, Speech, and Language Processing
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We present a novel approach to extract keyphrases based on Augmented Transition Networks (abbreviated as ATNs) followed by statistical methods from any given article, notes on a particular subject, or any other document source. The use of ATNs has completely ruled out the need of background corpora in identifying the potential keywords and keyphrases. Moreover, the use of ATNs has greatly reduced the search space for the statistical methods. We have devised two new methods namely, relaxed statistical analysis and stringent statistical analysis to identify the separability of phrases into sub phrases. In this paper, the two tier process is discussed in detail and illustrated with examples. We have also discussed the applications of this process briefly.