Work

Disambiguating False-Alarm Hashtag Usages in Tweets for Irony Detection

Year: 2018

Type: conference-paper

Abstract: The reliability of self-labeled data is an important issue when the data are regarded as ground-truth for training and testing learning-based models.This paper addresses the issue of false-alarm hashtags in the self-labeled data for irony detection.We analyze the ambiguity of hashtag usages and propose a novel neural networkbased model, which incorporates linguistic information from different aspects, to disambiguate the usage of three hashtags that are widely used to collect the training data for irony detection.Furthermore, we apply our model to prune the self-labeled training data.Experimental results show that the irony detection model trained on the less but cleaner training instances outperforms the models trained on all data.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p18-2122

Authors: Hen-Hsen Huang, Chiao-Chen Chen, Hsin-Hsi Chen

Institutions: National Taiwan University, National Science and Technology Council

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Ministry of Science and Technology, Taiwan

Awards: Information Recall Support for Elderly People in Hyper Aged Societies, Ai Support for Social and Mental Activities of the Elderly( I )