@inproceedings{tunike-etal-2023-ji,
title = "基于动态常识推理与多维语义特征的幽默识别(Humor Recognition based on Dynamically Commonsense Reasoning and Multi-Dimensional Semantic Features)",
author = "Tunike, Tuerxun and
Lin, Hongfei and
Zhang, Dongyu and
Yang, Liang and
Min, Changrong and
吐妮可, 吐尔逊 and
林, 鸿飞 and
张, 冬瑜 and
杨, 亮 and
闵, 昶荣",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-1.29/",
pages = "328--340",
language = "zho",
abstract = "{\textquotedblleft}随着社交媒体的飞速发展,幽默识别任务在近年来受到研究者的广泛关注。该任务的目标是判断给定的文本是否表达幽默。现有的幽默识别方法主要是在幽默产生理论的支撑下,利用规则或者设计神经网络模型来提取多种幽默相关特征,比如不一致性特征、情感特征以及语音特征等等。这些方法一方面说明情感信息在建模幽默语义当中的重要地位,另一方面说明幽默语义的构建依赖多个维度的特征。然而,这些方法没有充分捕捉文本内部的情感特征,忽略了幽默文本中的隐式情感表达,影响幽默识别的准确性。为了解决这一问题,本文提出一种动态常识与多维语义特征驱动的幽默识别方法CMSOR。该方法首先利用外部常识信息从文本中动态推理出说话者的隐式情感表达,然后引入外部词典WordNet计算文本内部词级语义距离进而捕捉不一致性,同时计算文本的模糊性特征。最后,根据上述三个特征维度构建幽默语义,实现幽默识别。本文在三个公开数据集上进行实验,结果表明本文所提方法CMSOR相比于当前基准模型有明显提升。{\textquotedblright}"
}
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<abstract>“随着社交媒体的飞速发展,幽默识别任务在近年来受到研究者的广泛关注。该任务的目标是判断给定的文本是否表达幽默。现有的幽默识别方法主要是在幽默产生理论的支撑下,利用规则或者设计神经网络模型来提取多种幽默相关特征,比如不一致性特征、情感特征以及语音特征等等。这些方法一方面说明情感信息在建模幽默语义当中的重要地位,另一方面说明幽默语义的构建依赖多个维度的特征。然而,这些方法没有充分捕捉文本内部的情感特征,忽略了幽默文本中的隐式情感表达,影响幽默识别的准确性。为了解决这一问题,本文提出一种动态常识与多维语义特征驱动的幽默识别方法CMSOR。该方法首先利用外部常识信息从文本中动态推理出说话者的隐式情感表达,然后引入外部词典WordNet计算文本内部词级语义距离进而捕捉不一致性,同时计算文本的模糊性特征。最后,根据上述三个特征维度构建幽默语义,实现幽默识别。本文在三个公开数据集上进行实验,结果表明本文所提方法CMSOR相比于当前基准模型有明显提升。”</abstract>
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%0 Conference Proceedings
%T 基于动态常识推理与多维语义特征的幽默识别(Humor Recognition based on Dynamically Commonsense Reasoning and Multi-Dimensional Semantic Features)
%A Tunike, Tuerxun
%A Lin, Hongfei
%A Zhang, Dongyu
%A Yang, Liang
%A Min, Changrong
%A 吐妮可, 吐尔逊
%A 林, 鸿飞
%A 张, 冬瑜
%A 杨, 亮
%A 闵, 昶荣
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G zho
%F tunike-etal-2023-ji
%X “随着社交媒体的飞速发展,幽默识别任务在近年来受到研究者的广泛关注。该任务的目标是判断给定的文本是否表达幽默。现有的幽默识别方法主要是在幽默产生理论的支撑下,利用规则或者设计神经网络模型来提取多种幽默相关特征,比如不一致性特征、情感特征以及语音特征等等。这些方法一方面说明情感信息在建模幽默语义当中的重要地位,另一方面说明幽默语义的构建依赖多个维度的特征。然而,这些方法没有充分捕捉文本内部的情感特征,忽略了幽默文本中的隐式情感表达,影响幽默识别的准确性。为了解决这一问题,本文提出一种动态常识与多维语义特征驱动的幽默识别方法CMSOR。该方法首先利用外部常识信息从文本中动态推理出说话者的隐式情感表达,然后引入外部词典WordNet计算文本内部词级语义距离进而捕捉不一致性,同时计算文本的模糊性特征。最后,根据上述三个特征维度构建幽默语义,实现幽默识别。本文在三个公开数据集上进行实验,结果表明本文所提方法CMSOR相比于当前基准模型有明显提升。”
%U https://aclanthology.org/2023.ccl-1.29/
%P 328-340
Markdown (Informal)
[基于动态常识推理与多维语义特征的幽默识别(Humor Recognition based on Dynamically Commonsense Reasoning and Multi-Dimensional Semantic Features)](https://aclanthology.org/2023.ccl-1.29/) (Tunike et al., CCL 2023)
ACL
- Tuerxun Tunike, Hongfei Lin, Dongyu Zhang, Liang Yang, Changrong Min, 吐尔逊 吐妮可, 鸿飞 林, 冬瑜 张, 亮 杨, and 昶荣 闵. 2023. 基于动态常识推理与多维语义特征的幽默识别(Humor Recognition based on Dynamically Commonsense Reasoning and Multi-Dimensional Semantic Features). In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 328–340, Harbin, China. Chinese Information Processing Society of China.