Challenge: Using a multi-layered scheme for the fine-grained annotation of irony on Italian Twitter is a challenging task to be performed by both human annotators and automatic NLP systems.
Approach: They propose to apply a multi-layered scheme for the fine-grained annotation of irony to an Italian Twitter corpus.
Outcome: The proposed scheme can be validated on Italian irony-laden social media contents and is available in the cross- and multi-lingual perspective.

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Challenge: Existing annotations for irony are difficult, and the recognition of it is difficult due to its polarity.
Approach: They propose a fine-grained annotation scheme centered on irony that highlights the tokens responsible for its activation and their morpho-syntactic features.
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An Italian Twitter Corpus of Hate Speech against Immigrants (L18-1)

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Challenge: a recent study has annotated 6,000 tweets for hate speech against immigrants . the annotation scheme was designed to account for the multiplicity of factors that can contribute to the definition of a hate speech notion .
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DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter (2020.lrec-1)

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Challenge: Current scholarship is yet to reach an agreement on a universal definition of the concept of irony.
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Multilingual Irony Detection with Dependency Syntax and Neural Models (2020.coling-main)

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Challenge: Several semantic and syntactic devices can be used to express irony, causing the incongruity, determine the clash and play the role of irony triggers within a text.
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An Algerian Corpus and an Annotation Platform for Opinion and Emotion Analysis (2020.lrec-1)

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Challenge: Currently, there are more than 4 billion Internet users worldwide . the number of social media users in Algeria has tripled over a year .
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PoSTWITA-UD: an Italian Twitter Treebank in Universal Dependencies (L18-1)

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Challenge: Various approaches and ad hoc resources are needed to provide proper coverage of specific linguistic phenomena.
Approach: They propose to annotate tweets using a well-known dependency-based annotation format . they propose to use the tweets for training NLP systems to improve their performance .
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An Annotated Social Media Corpus for German (2020.lrec-1)

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Challenge: Hate Speech (HS) against ethnic, religious and national minorities is a growing concern in online discourse.
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QUEEREOTYPES: A Multi-Source Italian Corpus of Stereotypes towards LGBTQIA+ Community Members (2024.lrec-main)

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Challenge: a dataset of social media texts addressing LGBTQIA+ individuals is presented in this paper . the dataset is based on two sources in italian: Facebook and Twitter .
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Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification (L18-1)

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Challenge: Existing corpus of Arabic textual data is limited to English or other European languages.
Approach: They present a large-scale and multi-dialectal corpus of Tweets from 11 regions and 16 countries in the arab world representing the major Arabic dialectal varieties.
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A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives (2022.coling-1)

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Challenge: figurative language research has focused on sarcasm and irony, but there is still a gap in the field.
Approach: They propose to review computational irony, cognitive science, and neural models of irony processing . they aim to encourage a balanced and equal research environment in figurative languages .
Outcome: The proposed multi-X irony processing perspectives will provide an overview of computational irony, insights from linguisic theory and cognitive science, and interactions with downstream NLP tasks.

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