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Unsupervised Keyphrase Extraction from Scientific Publications

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Computational Linguistics and Intelligent Text Processing (CICLing 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13451))

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Abstract

We propose a novel unsupervised keyphrase extraction approach that filters candidate keywords using outlier detection. It starts by training word embeddings on the _target document to capture semantic regularities among the words. It then uses the minimum covariance determinant estimator to model the distribution of non-keyphrase word vectors, under the assumption that these vectors come from the same distribution, indicative of their irrelevance to the semantics expressed by the dimensions of the learned vector representation. Candidate keyphrases only consist of words that are detected as outliers of this dominant distribution. Empirical results show that our approach outperforms state-of-the-art and recent unsupervised keyphrase extraction methods.

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Notes

  1. 1.

    https://github.com/stanfordnlp/GloVe.

  2. 2.

    https://www.nltk.org/.

  3. 3.

    https://scikit-learn.org.

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Authors and Affiliations

  1. Aristotle University of Thessaloniki, University Campus, 54124, Thessaloniki, Greece

    Eirini Papagiannopoulou & Grigorios Tsoumakas

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  1. Eirini Papagiannopoulou
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  2. Grigorios Tsoumakas
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Correspondence to Eirini Papagiannopoulou .

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  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Papagiannopoulou, E., Tsoumakas, G. (2023). Unsupervised Keyphrase Extraction from Scientific Publications. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_16

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  • DOI: https://doi.org/10.1007/978-3-031-24337-0_16

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