Keyphrase Extraction on Covid-19 Tweets Based on Doc2Vec and YAKE

Firdausillah, Fahri; Udayanti, Erika Devi

doi:10.33633/jais.v6i1.4454

Cited by 1 publication

(2 citation statements)

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“…is not dependent on the subject words, and it may produce less representative candidates. To address this problem, Firdausillah [35] introduced Doc2Vec, which belongs to the cross-document model, and can find similar documents in multiple ones based on the assumption of correlation and then combines Doc2Vec with YAKE! to calculate the similarity of the documents and merge similar ones.…”

Section: Unsupervised Methodsmentioning

confidence: 99%

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Y-Rank: A Multi-Feature-Based Keyphrase Extraction Method for Short Text

Liu,

Hui,

Liu

et al. 2024

Applied Sciences

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Keyphrase extraction is a critical task in text information retrieval, which traditionally employs both supervised and unsupervised approaches. Supervised methods generally rely on large corpora, which introduce the problems of availability, while unsupervised methods are independent of out-sources but also lead to defects like imperfect statistical features or low accuracy. Particularly in short-text scenarios, limited text features often result in low-quality candidate ranking. To address this issue, this paper proposes Y-Rank, a lightweight unsupervised keyphrase extraction method that extracts the average information content of candidate sentences as the key statistical features from a single document, and follows a graph construction approach based on similarity to obtain the semantic features of keyphrase with high-quality and ranking accuracy. Finally, the top-ranked keyphrases are acquired by the fusion of these features. The experimental results on five datasets illustrate that Y-Rank outperforms the other nine unsupervised methods, achieves enhancements on six accuracy metrics, including Precision, Recall, F-Measure, MRR, MAP, and Bpref, and performs the highest improvement in short text scenarios.

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Section: Unsupervised Methodsmentioning

confidence: 99%

“…Based on (7), Y-Rank merges statistical and semantic feature scores and outputs the ranked results in descending order (lines [32][33][34][35].…”

Section: Y-rank Implementationmentioning

confidence: 99%