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Authors: Simone D’Amico 1 ; Lorenzo Malandri 2 ; 3 ; Fabio Mercorio 2 ; 3 and Mario Mezzanzanica 2 ; 3

Affiliations: 1 Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy ; 2 Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy ; 3 CRISP Research Centre, University of Milan-Bicocca, Milan, Italy

Keyword(s): Keyphrases Extraction, Keyphrases Evaluation, Keyphrases Benchmark Evaluation, Word Embeddings, Natural Language Processing.

Abstract: A research area of NLP is known as keyphrases extraction, which aims to identify words and expressions in a text that comprehensively represent the content of the text itself. In this study, we introduce a new approach called KRAKEN (Keyphrease extRAction maKing use of EmbeddiNgs). Our method takes advantage of widely used NLP techniques to extract keyphrases from a text in an unsupervised manner and we compare the results with well-known benchmark datasets in the literature. The main contribution of this work is developing a novel approach for keyphrase extraction. Both natural language text preprocessing techniques and distributional semantics techniques, such as word embeddings, are used to obtain a vector representation of the texts that maintains their semantic meaning. Through KRAKEN, we propose and design a new method that exploits word embedding for identifying keyphrases, considering the relationship among words in the text. To evaluate KRAKEN, we employ benchmark datasets a nd compare our approach with state-of-the-art methods. Another contribution of this work is the introduction of a metric to rank the identified keyphrases, considering the relatedness of both the words within the phrases and all the extracted phrases from the same text. (More)

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Paper citation in several formats:
D’Amico, S.; Malandri, L.; Mercorio, F. and Mezzanzanica, M. (2023). KRAKEN: A Novel Semantic-Based Approach for Keyphrases Extraction. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR; ISBN 978-989-758-671-2; ISSN 2184-3228, SciTePress, pages 289-297. DOI: 10.5220/0012179500003598

@conference{kdir23,
author={Simone D’Amico. and Lorenzo Malandri. and Fabio Mercorio. and Mario Mezzanzanica.},
title={KRAKEN: A Novel Semantic-Based Approach for Keyphrases Extraction},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR},
year={2023},
pages={289-297},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012179500003598},
isbn={978-989-758-671-2},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR
TI - KRAKEN: A Novel Semantic-Based Approach for Keyphrases Extraction
SN - 978-989-758-671-2
IS - 2184-3228
AU - D’Amico, S.
AU - Malandri, L.
AU - Mercorio, F.
AU - Mezzanzanica, M.
PY - 2023
SP - 289
EP - 297
DO - 10.5220/0012179500003598
PB - SciTePress