sig
Full identifier: https://w3id.org/kpxl/ios/ds/np/RA1a4cPBEHBPYa2KQfhU5rgUmn5WPZwbFNobeJv9Oqw-U#sig
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Minted in Nanopublication
https://doi.org/10.3233/DS-220058
doi:10.3233/DS-220058
http://purl.org/dc/terms/abstract
has the abstract
(this is a literal)
"Sarcasm is a linguistic phenomenon often indicating a disparity between literal and inferred meanings. Due to its complexity, it is typically difficult to discern it within an online text message. Consequently, in recent years sarcasm detection has received considerable attention from both academia and industry. Nevertheless, the majority of current approaches simply model low-level indicators of sarcasm in various machine learning algorithms. This paper aims to present sarcasm in a new light by utilizing novel indicators in a deep weighted average ensemble-based framework (DWAEF). The novel indicators pertain to exploiting the presence of simile and metaphor in text and detecting the subtle shift in tone at a sentence’s structural level. A graph neural network (GNN) structure is implemented to detect the presence of simile, bidirectional encoder representations from transformers (BERT) embeddings are exploited to detect metaphorical instances and fuzzy logic is employed to account for the shift of tone. To account for the existence of sarcasm, the DWAEF integrates the inputs from the novel indicators. The performance of the framework is evaluated on a self-curated dataset of online text messages. A comparative report between the results acquired using primitive features and those obtained using a combination of primitive features and proposed indicators is provided. The highest accuracy of 92% was achieved after applying DWAEF, the proposed framework which combines the primitive features and novel indicators together as compared to 78.58% obtained using Support Vector Machine (SVM) which was the lowest among all classifiers."
.
The journal with ISSN
http://id.crossref.org/issn/2451-8492
2451-8492
http://purl.org/dc/terms/title
has the title
(this is a literal)
"Data Science"
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https://doi.org/10.3233/DS-220058
doi:10.3233/DS-220058
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doi:10.3233/DS-220058
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Richa Sharma
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Simrat Deol
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Udit Kaushish
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Prakher Pandey
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VISHAL MAURYA
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04gzb2213
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Prakher Pandey
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The assertion above
http://www.w3.org/ns/prov#wasAttributedTo
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Richa Sharma
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VISHAL MAURYA
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https://w3id.org/kpxl/ios/ds/np/RA1a4cPBEH...#assertion
The assertion above
http://www.w3.org/ns/prov#wasAttributedTo
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https://orcid.org/0000-0002-6785-9691
Simrat Deol
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This is the identifier for the assertion of this nanopublication.
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The assertion above
http://www.w3.org/ns/prov#wasAttributedTo
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Udit Kaushish
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This nanopublication
http://purl.org/dc/terms/license
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https://creativecommons.org/licenses/by/4.0/
Attribution 4.0 International (CC BY 4.0)
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this author list
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This is a local identifier minted within the nanopublication.
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This author list
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https://orcid.org/0000-0002-4472-1681
Richa Sharma
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This is a local identifier minted within the nanopublication.
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This author list
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Simrat Deol
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This is a local identifier minted within the nanopublication.
https://w3id.org/kpxl/ios/ds/np/RA1a4cPBEH...#author-list
This author list
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Udit Kaushish
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This is a local identifier minted within the nanopublication.
https://w3id.org/kpxl/ios/ds/np/RA1a4cPBEH...#author-list
This author list
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http://www.w3.org/1999/02/22-rdf-syntax-ns#_4
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Prakher Pandey
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This is a local identifier minted within the nanopublication.
https://w3id.org/kpxl/ios/ds/np/RA1a4cPBEH...#author-list
This author list
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VISHAL MAURYA
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https://orcid.org/0000-0002-1267-0234
Tobias Kuhn
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https://doi.org/10.3233/DS-220058
doi:10.3233/DS-220058
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Tobias Kuhn
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nanodash.petapico.org
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