Skip to main content
SpringerLink
Log in

Keyword Identification Using Text Graphlet Patterns

  • Conference paper
  • First Online:
Natural Language Processing and Information Systems (NLDB 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9612))

Abstract

Keyword identification is an important task that provides useful information for NLP applications including: document retrieval, clustering, and categorization, among others. State-of-the-art methods rely on local features of words (e.g. lexical, syntactic, and presentation features) to assess their candidacy as keywords. In this paper, we propose a novel keyword identification method that relies on representation of text abstracts as word graphs. The significance of the proposed method stems from a flexible data representation that expands the context of words to span multiple sentences and thus can enable capturing of important non-local graph topological features. Specifically, graphlets (small subgraph patterns) were efficiently extracted and scored to reflect the statistical dependency between these graphlet patterns and words labeled as keywords. Experimental results demonstrate the capability of the graphlet patterns in a keyword identification task when applied to MEDLINE, a standard research abstract dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Andrade, M.A., Valencia, A.: Automatic extraction of keywords from scientific text: application to the knowledge domain of protein families. Bioinformatics 14(7), 600–607 (1998)

    Article  Google Scholar 

  2. Matsuo, Y., Ishizuka, M.: Keyword extraction from a single document using word co-occurrence statistical information. Int. J. Artif. Intell. Tools 13(1), 157–169 (2004)

    Article  Google Scholar 

  3. Hammouda, K.M., Matute, D.N., Kamel, M.S.: CorePhrase: keyphrase extraction for document clustering. In: Perner, P., Imiya, A. (eds.) MLDM 2005. LNCS (LNAI), vol. 3587, pp. 265–274. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: ACL, pp. 1262–1273 (2005)

    Google Scholar 

  5. Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems, pp. 121–124. ACM (2013)

    Google Scholar 

  6. Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., Nevill-Manning, C.G.: KEA: practical automatic keyphrase extraction. In: Proceedings of the Fourth ACM Conference on Digital Libraries, pp. 254–255. ACM (1999)

    Google Scholar 

  7. Liu, F., Pennell, D., Liu, F., Liu, Y.: Unsupervised approaches for automatic keyword extraction using meeting transcripts. In: Proceedings of Human Language Technologies, pp. 620–628. Association for Computational Linguistics (2009)

    Google Scholar 

  8. Tomokiyo, T., Hurst, M.: A language model approach to keyphrase extraction. In: Proceedings of the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment, vol. 18, pp. 33–40. Association for Computational Linguistics (2003)

    Google Scholar 

  9. Zhang, Y., Zincir-Heywood, N., Milios, E.: Narrative text classification for automatic key phrase extraction in web document corpora. In: Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management, pp. 51–58. ACM (2005)

    Google Scholar 

  10. Mihalcea, R., Tarau, P.: TextRank: Bringing Order into Texts. Association for Computational Linguistics, Stroudsburg (2004)

    Google Scholar 

  11. Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)

    Google Scholar 

  12. Bellaachia, A., Al-Dhelaan, M.: Ne-rank: a novel graph-based keyphrase extraction in twitter. In: Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, vol. 01, pp. 372–379. IEEE Computer Society (2012)

    Google Scholar 

  13. Jiang, C., Coenen, F., Sanderson, R., Zito, M.: Text classification using graph mining-based feature extraction. Knowl. Based Syst. 23(4), 302–308 (2010)

    Article  Google Scholar 

  14. Blanco, R., Lioma, C.: Graph-based term weighting for information retrieval. Inf. Retrieval 15(1), 54–92 (2012)

    Article  Google Scholar 

  15. Pržulj, N.: Biological network comparison using graphlet degree distribution. Bioinformatics 23(2), e177–e183 (2007)

    Article  Google Scholar 

  16. Vacic, V., Iakoucheva, L.M., Lonardi, S., Radivojac, P.: Graphlet kernels for prediction of functional residues in protein structures. J. Comput. Biol. 17(1), 55–72 (2010)

    Article  MathSciNet  Google Scholar 

  17. Bird, S.: NLTK: the natural language toolkit. In: Proceedings of the COLING/ACL on Interactive Presentation Sessions, pp. 69–72. Association for Computational Linguistics (2006)

    Google Scholar 

  18. Aizawa, A.: An information-theoretic perspective of TF-IDF measures. Inf. Process. Manage. 39(1), 45–65 (2003)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Fayoum University, Faiyum, Egypt

    Ahmed Ragab Nabhan

  2. Member Technology, Sears Holdings, Hoffman Estates, USA

    Ahmed Ragab Nabhan

  3. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  4. School of Informatics, University of Edinburgh, Edinburgh, UK

    Khaled Shaalan

Authors
  1. Ahmed Ragab Nabhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khaled Shaalan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed Ragab Nabhan .

Editor information

Editors and Affiliations

  1. ConservatoireNational desArts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Salford, Salford, United Kingdom

    Farid Meziane

  3. University of Salford, Salford, United Kingdom

    Mohamad Saraee

  4. Oakland University, Rochester, Michigan, USA

    Vijayan Sugumaran

  5. University of Salford, Salford, United Kingdom

    Sunil Vadera

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Nabhan, A.R., Shaalan, K. (2016). Keyword Identification Using Text Graphlet Patterns. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-41754-7_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41753-0

  • Online ISBN: 978-3-319-41754-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation