nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

Venn Diagram-Based Feature Ranking Technique for Key Term Extraction

verfasst von : Neelotpal Chakraborty, Sambit Mukherjee, Ashes Ranjan Naskar, Samir Malakar, Ram Sarkar, Mita Nasipuri

Erschienen in: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Classification of text documents from a pool of huge collection of the same is performed usually on the basis of certain key terms present in the said documents that distinguish a particular document set from the universal set. Generally, these key terms are identified using some feature sets, which can be statistical, rule-based, linguistic, or hybrid in nature. This paper develops a simple technique based on Venn diagram to prioritize the different standard features available in the literature, which in turn reduces the dimension of the feature sets used for document classification.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Neighborhood Topology to Discover Influential Nodes in a Complex Network

Nächstes Kapitel Bangla Handwritten City Name Recognition Using Gradient-Based Feature

Chakraborty, Neelotpal, Samir Malakar, Ram Sarkar, Mita Nasipuri. “A Rule based Approach for Noun Phrase Extraction from English Text Document.” 2016 Seventh International Conference on CNC. CNC, 2016.

Han, Jiawei, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques. Elsevier, 2011.

Hasan, Kazi Saidul, and Vincent Ng. “Automatic Keyphrase Extraction: A Survey of the State of the Art.” ACL (1). 2014.

Mangina, Eleni, and John Kilbride. “Evaluation of keyphrase extraction algorithm and tiling process for a document/resource recommender within e-learning environments.” Computers & Education 50.3 (2008): 807–820.

Haddoud, Mounia, and Saïd Abdeddaïm. “Accurate keyphrase extraction by discriminating overlapping phrases.” Journal of Information Science (2014): 0165551514530210.

Jurafsky, Dan, and James H. Martin. Speech and language processing. Pearson, 2014.

Turney, Peter D. “Learning algorithms for keyphrase extraction.” Information Retrieval 2.4 (2000): 303–336.

Witten, Ian H., et al. “KEA: Practical automatic keyphrase extraction.” Proceedings of the fourth ACM conference on Digital libraries. ACM, 1999.

Sarkar, Kamal, Mita Nasipuri, and Suranjan Ghose. “Machine learning based keyphrase extraction: comparing decision trees, naïve Bayes, and artificial neural networks.” Journal of Information Processing Systems 8.4 (2012): 693–712.

10.

Yu, Feng, Hong-Wei Xuan, and De-quan Zheng. “Key-Phrase Extraction Based on a Combination of CRF Model with Document Structure.” Computational Intelligence and Security (CIS), 2012 Eighth International Conference on. IEEE, 2012.

11.

Sarawagi, Sunita, and William W. Cohen. “Semi-markov conditional random fields for information extraction.” Advances in neural information processing systems. 2004.

12.

Beliga, Slobodan, Ana Meštrović, and Sanda Martinčić-Ipšić. “An Overview of Graph-Based Keyword Extraction Methods and Approaches.” Journal of Information and Organizational Sciences 39.1 (2015): 1–20.

13.

Dharmadhikari, Shweta C., Maya Ingle, and Parag Kulkarni. “Empirical Studies on Machine Learning Based Text Classification Algorithms.” Advanced Computing 2.6 (2011): 161.

14.

Jiang, Xin, Yunhua Hu, and Hang Li. “A ranking approach to keyphrase extraction.” Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, 2009.

15.

Siddiqi, Sifatullah, and Aditi Sharan. “Keyword and Keyphrase Extraction Techniques: A Literature Review.” International Journal of Computer Applications 109.2 (2015).

16.

Kaur, Jasmeen, and Vishal Gupta. “Effective approaches for extraction of keywords.” Journal of Computer Science 7.6 (2010): 144–148.

Titel: Venn Diagram-Based Feature Ranking Technique for Key Term Extraction
verfasst von: Neelotpal Chakraborty
Sambit Mukherjee
Ashes Ranjan Naskar
Samir Malakar
Ram Sarkar
Mita Nasipuri
Verlag: Springer Singapore
Buch: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications
Print ISBN: 978-981-10-3152-6

Electronic ISBN: 978-981-10-3153-3

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-981-10-3153-3_33

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner