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Unsupervised Automatic Keyphrases Extraction Algorithms

Experimentations on Paintings
  • Isabella GagliardiEmail author
  • Maria Teresa Artese
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11231)

Abstract

Massive volumes of images of museums or art collections, or made available by artists and photographers, more and more often, are available on the web, along with some metadata, essential for their characterization and retrieval. A set of (scored) keywords/keyphrases that characterize the semantic content of the documents should be, automatically or manually, extracted and/or associated. We present here a work-in-progress to evaluate different methods for the unsupervised keyword extraction to Italian and English datasets. In the paper datasets, algorithms and approaches are presented and discussed together with some preliminary results referred to relatedness of terms.

Keywords

Unsupervised Automatic Keyphrase Extraction Evaluation Information Retrieval TextRank RAKE Tf-Idf Latent Semantic Indexing 

References

  1. 1.
    Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of the 52nd Annual Meeting of the ACL, vol. 1, pp. 1262–1273. ACL Baltimore, Maryland (2014)Google Scholar
  2. 2.
    Siddiqi, S., Sharan, A.: Keyword and keyphrase extraction techniques: a literature review. Int. J. Comput. Appl. 109(2), 18–23 (2015).  https://doi.org/10.5120/19161-0607CrossRefGoogle Scholar
  3. 3.
    Schütze, H., Manning, C.D., Raghavan, P.: Introduction to Information Retrieval, vol. 39. Cambridge University Press, Cambridge (2008)zbMATHGoogle Scholar
  4. 4.
    Mihalcea, R., Tarau, P.: Textrank: bringing order into text. In: Lin, D., Wu, D. (eds.) Proceedings of EMNLP 2004, pp. 404–411. ACL (2004)Google Scholar
  5. 5.
    Rose, S., Engel, D., Cramer, N., Cowley, W.: Automatic keyword extraction from individual documents. In: Berry, M.W., Kogan, J. (eds.) Text Mining: Applications and Theory, pp. 1–20. Wiley, Hoboken (2010).  https://doi.org/10.1002/9780470689646.ch1CrossRefGoogle Scholar
  6. 6.
    Wan, X., Xiao, J.: Single document keyphrase extraction using neighborhood knowledge. In: AAAI-08, pp. 855–860. AAAI Press, Menlo Park, California (2008)Google Scholar
  7. 7.
    Wan, X., Xiao, J.: CollabRank: towards a collaborative approach to single-document keyphrase extraction. In: Proceedings of the 22nd International Conference on Computational Linguistics, vol. 1, pp. 969–976. ACL (2008)Google Scholar
  8. 8.
    Liu, Z., Li, P., Zheng, Y., Sun, M.: Clustering to find exemplar terms for keyphrase extraction. In: Proceedings of EMNLP 2010, vol. 1, pp. 257–266. ACL Cambridge, MA (2010)Google Scholar
  9. 9.
    Alrehamy, H.H., Walker, C.: SemCluster: unsupervised automatic keyphrase extraction using affinity propagation. In: Chao, F., Schockaert, S., Zhang, Q. (eds.) UKCI 2017. AISC, vol. 650, pp. 222–235. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-66939-7_19CrossRefGoogle Scholar
  10. 10.
    Bracewell, D.B., Ren, F., Kuriowa, S.: Multilingual single document keyword extraction for information retrieval. In: Proceeding of IEEE NLP-KE’05, pp. 517–522 (2015).  https://doi.org/10.1109/NLPKE.2005.1598792
  11. 11.
    Dumais, S.T.: Latent semantic analysis. Ann. Rev. Info. Sci. Tech. 38, 188–230 (2005).  https://doi.org/10.1002/aris.1440380105CrossRefGoogle Scholar
  12. 12.
    Khan, F.S., Beigpour, S., Van de Weijer, J., Felsberg, M.: Painting-91: a large scale database for computational painting categorization. Mach. Vis. Appl. 25, 1385 (2014).  https://doi.org/10.1007/s00138-014-0621-6CrossRefGoogle Scholar
  13. 13.
    Artese, M.T., Ciocca, G., Gagliardi, I.: Evaluating perceptual visual attributes in social and cultural heritage web sites. J. Cultural Herit. 26, 91–100 (2017).  https://doi.org/10.1016/j.culher.2017.02.009CrossRefGoogle Scholar
  14. 14.
    Artese, M.T., Gagliardi, I.: What is this painting about? Experiments on unsupervised keyphrases extraction algorithms. In: IOP Conference Series: Materials Science and Engineering, vol. 364, p. 012050 (2018).  https://doi.org/10.1088/1757-899x/364/1/012050CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CNR IMATI – MilanoMilanItaly

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