ML Anthology

KRAKEN: A Novel Semantic-Based Approach for Keyphrases Extraction

Simone D'Amico
IJCAI 2022 pp. 5845-5846
doi:10.24963/IJCAI.2022/825 /ijcai/2022/daposamico2022ijcai-kraken/

Abstract

We propose KRAKEN, a novel approach for the extraction of keyphrases from texts. To this aim, KRAKEN makes use of distributional semantics to identify, as completely as possible, representative portions of documents, i.e. keyphrases. In addition, we define novel metrics to assess a weighted significance to the keyphrases extracted from a document, identifying the most important ones by assessing their semantic similarity with the text of the document they belong to.

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Cite

Text

D'Amico. "KRAKEN: A Novel Semantic-Based Approach for Keyphrases Extraction." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/825

Markdown

[D'Amico. "KRAKEN: A Novel Semantic-Based Approach for Keyphrases Extraction." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/daposamico2022ijcai-kraken/) doi:10.24963/IJCAI.2022/825

BibTeX

@inproceedings{daposamico2022ijcai-kraken,
  title     = {{KRAKEN: A Novel Semantic-Based Approach for Keyphrases Extraction}},
  author    = {D'Amico, Simone},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {5845-5846},
  doi       = {10.24963/IJCAI.2022/825},
  url       = {https://mlanthology.org/ijcai/2022/daposamico2022ijcai-kraken/}
}