#HashtagWars：学习幽默感
#HashtagWars: Learning a Sense of Humor
摘要：
在这项工作中，我们提出了一个新的数据集计算幽默，特别是比较幽默排名，试图避开无处不在的二进制方法来幽默检测。该数据集由对给定标签的幽默回应的推文组成。我们描述了这个新数据集的动机以及收集过程，其中包括对我们的半自动数据汇集系统的描述。我们还提出了使用无监督和有监督的方法对该数据集进行初始实验。我们最好的监督系统达到了63.7%的准确率，这表明这项任务比类似的幽默检测任务要困难得多。最初的实验表明，字符级模型比令牌级模型更适合此任务，这可能是由于字符级模型可以捕获大量的双关语。
英文摘要：
In this work, we present a new dataset for computational humor, specifically comparative humor ranking, which attempts to eschew the ubiquitous binary approach to humor detection. The dataset consists of tweets that are humorous responses to a given hashtag. We describe the motivation for this new dataset, as well as the collection process, which includes a description of our semi-automated system for data collection. We also present initial experiments for this dataset using both unsupervised and supervised approaches. Our best supervised system achieved 63.7% accuracy, suggesting that this task is much more difficult than comparable humor detection tasks. Initial experiments indicate that a character-level model is more suitable for this task than a token-level model, likely due to a large amount of puns that can be captured by a character-level model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
