Work

ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods

Year: 2018

Type: conference-paper

Abstract: The paper describes our submissions to task 3 in SemEval 2018.There are two subtasks: Subtask A is a binary classification task to determine whether a tweet is ironic, and Subtask B is a fine-grained classification task including four classes.To address them, we explored supervised machine learning method alone and in combination with neural networks.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/s18-1098

Authors: Zhenghang Yin, Feixiang Wang, Man Lan, Wenting Wang

Institutions: East China Normal University, Alibaba Group (China)

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Science and Technology Commission of Shanghai Municipality

OpenAlex ID: W2805837477