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Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)最新文献

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Dataset Analysis and Augmentation for Emoji-Sensitive Irony Detection 表情符号敏感反语检测的数据集分析与增强
Pub Date : 2019-11-01 DOI: 10.18653/v1/d19-5527
Shirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, A. Black
Irony detection is an important task with applications in identification of online abuse and harassment. With the ubiquitous use of non-verbal cues such as emojis in social media, in this work we aim to study the role of these structures in irony detection. Since the existing irony detection datasets have <10% ironic tweets with emoji, classifiers trained on them are insensitive to emojis. We propose an automated pipeline for creating a more balanced dataset.
反语检测是一项重要的任务,在识别网络滥用和骚扰方面有着广泛的应用。随着社交媒体中表情符号等非语言线索的普遍使用,本研究旨在研究这些结构在反语检测中的作用。由于现有的反讽检测数据集含有表情符号的反讽推文不到10%,因此在这些数据集上训练的分类器对表情符号不敏感。我们提出了一个自动化的管道来创建一个更平衡的数据集。
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引用次数: 3
CodeSwitch-Reddit: Exploration of Written Multilingual Discourse in Online Discussion Forums CodeSwitch-Reddit:在线论坛中多语言文字话语的探索
Pub Date : 1900-01-01 DOI: 10.18653/v1/D19-5558
Ella Rabinovich, Masih Sultani, S. Stevenson
In contrast to many decades of research on oral code-switching, the study of written multilingual productions has only recently enjoyed a surge of interest. Many open questions remain regarding the sociolinguistic underpinnings of written code-switching, and progress has been limited by a lack of suitable resources. We introduce a novel, large, and diverse dataset of written code-switched productions, curated from topical threads of multiple bilingual communities on the Reddit discussion platform, and explore questions that were mainly addressed in the context of spoken language thus far. We investigate whether findings in oral code-switching concerning content and style, as well as speaker proficiency, are carried over into written code-switching in discussion forums. The released dataset can further facilitate a range of research and practical activities.
与几十年来对口头语码转换的研究相比,对书面多语言作品的研究直到最近才引起了人们的兴趣。关于书面语码转换的社会语言学基础仍然存在许多悬而未决的问题,并且由于缺乏适当的资源,进展受到限制。我们引入了一个新颖、庞大、多样的书面代码转换作品数据集,这些数据集来自Reddit讨论平台上多个双语社区的主题主题,并探索了迄今为止主要在口语背景下解决的问题。我们调查了口头语码转换中有关内容和风格以及说话者熟练程度的发现是否会延续到论坛中的书面语码转换中。发布的数据集可以进一步促进一系列研究和实践活动。
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引用次数: 0
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Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
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