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Found 7 papers, 3 papers with code
``Laughing at you or with you'': The Role of Sarcasm in Shaping the Disagreement Space
We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e. g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures).
"Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space
We exploit joint modeling in terms of (a) applying discrete features that are useful in detecting sarcasm to the task of argumentative relation classification (agree/disagree/none), and (b) multitask learning for argumentative relation classification and sarcasm detection using deep learning architectures (e. g., dual Long Short-Term Memory (LSTM) with hierarchical attention and Transformer-based architectures).
Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention
The topical stance detection problem addresses detecting the stance of the text content with respect to a given topic: whether the sentiment of the given text content is in FAVOR of (positive), is AGAINST (negative), or is NONE (neutral) towards the given topic.
EmTaggeR: A Word Embedding Based Novel Method for Hashtag Recommendation on Twitter
The top-K hashtags that appear in this ranked list, are recommended for the given test post.
A Big Data Analysis Framework Using Apache Spark and Deep Learning
In this paper, we propose a novel framework that combines the distributive computational abilities of Apache Spark and the advanced machine learning architecture of a deep multi-layer perceptron (MLP), using the popular concept of Cascade Learning.
A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs
We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter.
