{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nihrio-at-semeval-2018-task-3-a-simple-and","title":"NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter","arxiv_id":"1804.00520","date":"2018-04-02","proceeding":"SEMEVAL 2018 6","authors":["Thanh Vu","Dat Quoc Nguyen","Xuan-Son Vu","Dai Quoc Nguyen","Michael Catt","Michael Trenell"],"abstract":"This paper describes our NIHRIO system for SemEval-2018 Task 3 \"Irony\ndetection in English tweets\". We propose to use a simple neural network\narchitecture of Multilayer Perceptron with various types of input features\nincluding: lexical, syntactic, semantic and polarity features. Our system\nachieves very high performance in both subtasks of binary and multi-class irony\ndetection in tweets. In particular, we rank third using the accuracy metric and\nfifth using the F1 metric. Our code is available at\nhttps://github.com/NIHRIO/IronyDetectionInTwitter","url_abs":"http://arxiv.org/abs/1804.00520v2","url_pdf":"http://arxiv.org/pdf/1804.00520v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nihrio-at-semeval-2018-task-3-a-simple-and","repo_url":"https://github.com/NIHRIO/IronyDetectionInTwitter","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}