从语言和视觉信息中学习表情符号和Kaomojis之间的桥梁

Jingun Kwon, Naoki Kobayashi, Hidetaka Kamigaito, Hiroya Takamura, M. Okumura
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引用次数: 2

摘要

表情符号的小图像具有独特的特征,可以作为理解作者意图的附加信息。它们使社交媒体用户能够强调自己的情绪,并在帖子中表达手势动作。除了表情符号,kaomojis(表情符号或面部标记)也有类似的功能。它们是由一系列字符组成的,尤其在亚洲国家很流行。虽然表情符号和表情包功能相似,含义相同,可以作为意见挖掘或情感分析的线索,但以往的研究偏向于将表情符号和表情包分开研究。在本文中,我们将emojis和kaomojis作为一个单一的令牌放在一起,在日语上下文中提供它们之间的桥梁。具体来说,我们的目的是判断表情符号和kaomojis是否具有相同的含义或彼此相似。我们假设emojis和kaomojis都是一个单词,以便通过skip-gram模型获得它们的语言信息。此外,我们提出了一种新的方法来考虑表情符号和kaomojis本身的外观,这意味着我们探索了它们视觉上相似的形状的信息。我们将它们都视为一张图像,以便在CNN模型中考虑它们的视觉信息。我们将两种不同的视角融合在一起,在同一空间内同时探索表情符号和考莫符号的语言和视觉信息。实验结果表明,我们可以将无限数量的表情符号和kaomojis与其表征(嵌入)对齐,并且在语言信息的基础上添加视觉信息可以改善其表征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bridging Between Emojis and Kaomojis by Learning Their Representations from Linguistic and Visual Information
Small images of emojis have unique characteristics as additional information in understanding writers’ intentions. They enable social media users to emphasize their emotions and to express gestural movements in their posts. In addition to emojis, kaomojis (emoticons or facemarks) also behave in a similar way. They are composed of a sequence of characters, which are popularized especially in Asian countries. Although both emojis and kaomojis fulfill similar functions and share the same meaning that can be clues in opinion mining or sentiment analysis, the previous researches have been biased to explore emojis and kaomojis separately. In this paper, we align emojis and kaomojis together as a single token in the Japanese context to offer a bridge between them. Specifically, we aim to judge whether emojis and kaomojis share the same meaning or are similar with each other. We assume that emojis and kaomojis are both a single word in order to obtain their linguistic information with the skip-gram model. Furthermore, we present a new approach to consider the appearances of emojis and kaomojis in themselves, meaning that we explore the information of their visually similar shapes. We regard both of them as a single image to take into account their visual information with the CNN model. We merge two different perspectives toward emojis and kaomojis by exploring their linguistic and visual information simultaneously on the same space. The experimental results showed that we can align an unlimited number of emojis and kaomojis together with their representations (embeddings), and adding the visual information to the linguistic information can improve their representations.
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