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Deep Fusion of Multiple Term-Similarity Measures For Biomedical Passage Retrieval
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| 22 |
A Twitter Political Corpus of the 2019 10N Spanish Election
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| 23 |
Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets
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Abstract:
[EN] In the last years, the control of online user generated content is becoming a priority, because of the increase of online aggressiveness and hate speech legal cases. Considering the complexity and the importance of this issue, this paper presents an approach that combines the deep learning framework with linguistic features for the recognition of aggressiveness in Mexican tweets. This approach has been evaluated relying on a collection of tweets released by the organizers of the shared task about aggressiveness detection in the context of the Ibereval 2018 evaluation campaign. The use of a benchmark corpus allows to compare the results with those obtained by Ibereval 2018 participant systems. However, looking at the achieved results, linguistic features seem not to help the deep learning classification for this task. ; The work of Simona Frenda and Paolo Rosso was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P). ; Frenda, S.; Banerjee, S.; Rosso, P.; Patti, V. (2020). Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets. Computación y Sistemas. 24(2):633-643. https://doi.org/10.13053/CyS-24-2-3398 ; S ; 633 ; 643 ; 24 ; 2
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Keyword:
Aggressiveness automatic detection; Deep learning; LENGUAJES Y SISTEMAS INFORMATICOS; Linguistic analysis; Mexican Spanish language; Twitter
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URL: https://doi.org/10.13053/CyS-24-2-3398
http://hdl.handle.net/10251/166375
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| 24 |
Multimodal Fake News Detection with Textual, Visual and Semantic Information
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An Emotional Analysis of False Information in Social Media and News Articles
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Irony Detection in Twitter with Imbalanced Class Distributions
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| 27 |
#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection
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Fine-Grained Analysis of Language Varieties and Demographics
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| 30 |
Multilingual Stance Detection in Social Media Political Debates
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| 31 |
Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?
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Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection
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The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers
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Scalable and Language-Independent Embedding-based Approach for Plagiarism Detection Considering Obfuscation Type: No Training Phase
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| 36 |
Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions
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| 39 |
Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter
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IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets
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