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Found 9 papers, 1 papers with code
IISERB Brains at SemEval-2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English
This paper describes the system architectures and the models submitted by our team “IISERB Brains” to SemEval 2022 Task 6 competition.
IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English
This paper describes the system architectures and the models submitted by our team "IISERBBrains" to SemEval 2022 Task 6 competition.
A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News
There is a huge difference between a scientific journal reporting {`}wine consumption might be correlated to cancer{'}, and a media outlet publishing {`}wine causes cancer{'} citing the journal{'}s results.
Code-switching patterns can be an effective route to improve performance of downstream NLP applications: A case study of humour, sarcasm and hate speech detection
In this paper we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications.
A deep-learning framework to detect sarcasm targets
In this paper we propose a deep learning framework for sarcasm target detection in predefined sarcastic texts.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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KGPChamps at SemEval-2019 Task 3: A deep learning approach to detect emotions in the dialog utterances.
We report the bidirectional LSTM model, along with the input word embedding as the concatenation of word embedding generated from bidirectional LSTM for word characters and conceptnet embedding, as the best performing model with a highest micro-F1 score of 0. 7261.
All that is English may be Hindi: Enhancing language identification through automatic ranking of the likeliness of word borrowing in social media
Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts.
All that is English may be Hindi: Enhancing language identification through automatic ranking of likeliness of word borrowing in social media
Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts.
Is this word borrowed? An automatic approach to quantify the likeliness of borrowing in social media
We first propose context based clustering method to sample a set of candidate words from the social media data. Next, we propose three novel and similar metrics based on the usage of these words by the users in different tweets; these metrics were used to score and rank the candidate words indicating their borrowed likeliness.
