https://scholars.lib.ntu.edu.tw/handle/123456789/413080
標題: | Disambiguating false-alarm hashtag usages in tweets for irony detection | 作者: | Huang H.-H. Chen C.-C. Chen H.-H. |
公開日期: | 2018 | 卷: | 2 | 起(迄)頁: | 771-777 | 來源出版物: | 56th Annual Meeting of the Association for Computational Linguistics | 摘要: | 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 network-based 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. ? 2018 Association for Computational Linguistics |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413080 | ISBN: | 9781948087346 |
顯示於: | 資訊工程學系 |
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