可一个算法检测嘲讽 [英] Can an algorithm detect sarcasm

问题描述

我被要求写一个算法来检测嘲讽，但我遇到了一个缺陷（或什么似乎像一个）的逻辑。

I was asked to write an algorithm to detect sarcasm but I came across a flaw (or what seems like one) in the logic.

例如，如果一个人说：

答：我喜欢贾斯汀Beiber。你喜欢他？

A: I love Justin Beiber. Do you like him to?

B：是啊。当然。 我绝对爱他。

B: Yeah. Sure. I absolutely love him.

现在，这可能被认为是讽刺或不并知道的唯一途径似乎知道，如果B是严重与否。

Now this may be considered sarcasm or not and the only way to know seems to be to know if B is serious or not.

（我是不是应该在深度。我们得到了一堆词组，只是被告知，如果这些都是在句子则是讽刺，但我有兴趣？）

(I wasn't supposed to be in depth. We were given a bunch of phrases and just were told that if these were in the sentence then it was sarcastic but I got interested?)

有没有什么办法来解决呢？或者是电脑完全卡住，当涉及到讥讽？

Is there any way to work around this? Or are computers absolutely stuck when it comes to sarcasm?

（我想这取决于说话者的语气，但我的输入文字）

(I suppose it depends on the tone of the speaker but my input is text)

推荐答案

看起来像有研究，尝试了这一点，但他们还没有拿出一个良好的工作算法。

Looks like there are studies that attempted just that, but they have yet to come up with a well working algorithm.

从<一个href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.207.5253&rep=rep1&type=pdf">González-Ibáñez, R.等。 识别讽刺的微博：仔细看看

挖苦和讽刺是语言学深入研究的现象，   心理学和认知科学[...]。但是在文本挖掘   文学，自动检测嘲讽被认为是一个困难的   问题[...]和   已得到解决的只有少数的研究。 [...]最密切相关的？我们的工作就是达维多夫等人。   （2010年），其目的是识别讽刺和非讽刺   话语在Twitter和亚马逊的产品评论。在本文中，我们   考虑来自非讽刺鸣叫

Sarcasm and irony are well-studied phenomena in linguistics, psychology and cognitive science[...]. But in the text mining literature, automatic detection of sarcasm is considered a difficult problem [...] and has been addressed in only a few studies. [...] The work most closely related to ours is that of Davidov et al. (2010), whose objective was to identify sarcastic and non-sarcastic utterances in Twitter and in Amazon product reviews. In this paper, we consider the somewhat harder problem of distinguishing sarcastic tweets from non- sarcastic tweets

他们得出结论：

也许并不奇怪，无论是人的评委，也没有机器   学习技术表现非常好。 [...]我们的研究结果表明，词汇特征本身不足以识别讽刺和务实情境特征值得进一步研究

Perhaps unsurprisingly, neither the human judges nor the machine learning techniques perform very well. [...] Our results suggest that lexical features alone are not sufficient for identifying sarcasm and that pragmatic and contextual features merit further study

下面是另一种最新的，相关文章：
雷耶斯，答：从幽默承认讽刺检测：社会化媒体的网络gurative语言

Here is another recent, relevant paper:
Reyes, A. "From humor recognition to irony detection: The figurative language of social media"

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