Human communication often involves the use of irony. In many cases, it is far from obvious if an utterance is meant ironical or not. Context and world knowledge are needed to discriminate literal from ironic intent.
Linguists have worked on describing the nature of irony and come up with ideas which reflect the intuitive understanding of irony.
Parallely, computational linguists are confronted with the challenge of automatically detecting irony. When an utterance contains irony, the only chance of getting the intent, is understanding and interpreting the irony in it.
I review different theories of irony in chapter 2.
Chapter 3 describes the state-of-the-art of automatic irony detection, covers the importance of corpus study for future research and proposes a fusion between theory, corpus study and automatic detection.
Contents
1 Introduction
2 Linguistic theories of irony
2.1 Echoic mention theory
2.2 Pretense theory
2.3 Allusional pretense theory
2.4 Unified theory
2.5 Discussion
3 Automatic detection of irony
3.1 “Yeah Right”: Sarcasm Recognition for Spoken Dialogue Systems .
3.1.1 Material
3.1.2 Method
3.1.3 Results
3.1.4 Discussion
3.2 Lexical Influences on the Perception of Sarcasm
3.2.1 Material
3.2.2 Method
3.2.3 Results
3.2.4 Discussion
3.3 Using LSA to detect Irony
3.3.1 Material
3.3.2 Method
3.3.3 Results
3.3.4 Discussion
3.4 Clues for Detecting Irony in User-Generated Contents: Oh...!! It’s “so easy” ;-)
3.4.1 Material
3.4.2 Method
3.4.3 Results
3.4.4 Discussion
3.5 Detecting Ironic Intent in Creative Comparisons
3.5.1 Material
3.5.2 Method
3.5.3 Results
3.5.4 Discussion
3.6 Automatic Satire Detection: Are You Having a Laugh?
3.6.1 Material
3.6.2 Method
3.6.3 Results
3.6.4 Discussion
3.7 Semi-Supervised Recognition of Sarcastic Sentences in Twitter and Amazon
3.7.1 Material
3.7.2 Method
3.7.3 Results
3.7.4 Discussion
3.8 Summary
4 Future Work
References