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Modelling Context with User Embeddings for Sarcasm Detection in Social Media
Silvio Amir
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Byron C. Wallace
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Hao Lyu
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Paula Carvalho
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Mário J. Silva
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Paper Details:
Month: August
Year: 2016
Location: Berlin, Germany
Venue:
CoNLL |
SIG: SIGNLL
Citations
URL
Tweet Sarcasm Detection Using Deep Neural Network
Meishan Zhang
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Yue Zhang
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Guohong Fu
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SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model
Shuying Lin
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Huosheng Xie
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Liang-Chih Yu
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K. Robert Lai
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A Large Self-Annotated Corpus for Sarcasm
Mikhail Khodak
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Nikunj Saunshi
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Kiran Vodrahalli
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SemEval-2018 Task 3: Irony Detection in English Tweets
Cynthia Van Hee
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Els Lefever
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Véronique Hoste
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KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection
Luise Dürlich
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YNU-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on Twitter
Bo Peng
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Jin Wang
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Xuejie Zhang
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‘Who would have thought of that!’: A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection
Aditya Joshi
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Prayas Jain
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Pushpak Bhattacharyya
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Mark Carman
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Improved Abusive Comment Moderation with User Embeddings
John Pavlopoulos
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Prodromos Malakasiotis
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Juli Bakagianni
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Ion Androutsopoulos
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Detecting Sarcasm is Extremely Easy ;-)
Natalie Parde
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Rodney Nielsen
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Using Author Embeddings to Improve Tweet Stance Classification
Adrian Benton
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Mark Dredze
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http://reddit.com
https://github.com/samiroid/CUE-CNN
Field Of Study
Linguistic Trends
Embeddings
Language
English
Dataset
News
Social Media
Twitter
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