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.
Table of 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
- 3.1 "Yeah Right": Sarcasm Recognition for Spoken Dialogue Systems
- 4 Future Work
Objectives and Key Themes
This coursework aims to explore the multifaceted nature of verbal irony, examining existing linguistic theories and the current state of automatic irony detection. It investigates the challenges inherent in computationally identifying ironic intent within various contexts.
- Linguistic Theories of Irony
- Automatic Irony Detection Methods
- Challenges in Computational Irony Recognition
- Corpus Studies and their Importance
- The relationship between irony and sarcasm
Chapter Summaries
1 Introduction: This introductory chapter sets the stage by defining verbal irony and differentiating it from other forms of irony. It uses Alanis Morissette's song lyrics as an example of situational and verbal irony, highlighting the contrast between literal meaning and intended meaning. The chapter emphasizes the difficulty of automatically detecting irony due to the reliance on context and world knowledge and outlines the structure of the coursework, focusing on reviewing existing linguistic theories and exploring the advancements in automatic irony detection.
2 Linguistic theories of irony: This chapter delves into various linguistic theories attempting to explain the nature of irony. It begins by examining Grice's theory of pragmatics, highlighting how ironic utterances can violate his maxims of quality, quantity, relevance, and manner. The chapter then critiques the Gricean account by addressing issues such as the oversimplification of ironic meaning as simply the opposite of literal meaning, the limitations in explaining rhetorical questions, and the challenges posed by the absence of tonal cues in written text. The chapter sets the groundwork for understanding the complexities of irony before moving into computational approaches.
Keywords
Verbal irony, sarcasm, automatic irony detection, linguistic theories, Gricean maxims, computational linguistics, corpus studies, natural language processing, machine learning, context, intent.
Frequently Asked Questions: A Comprehensive Language Preview on Irony Detection
What is the main topic of this coursework?
This coursework explores the multifaceted nature of verbal irony, focusing on existing linguistic theories and the current state of automatic irony detection. It investigates the challenges of computationally identifying ironic intent in various contexts.
What are the key themes covered in this coursework?
The key themes include linguistic theories of irony, automatic irony detection methods, challenges in computational irony recognition, corpus studies and their importance, and the relationship between irony and sarcasm.
What linguistic theories of irony are discussed?
The coursework examines several linguistic theories, including Grice's theory of pragmatics and its limitations in explaining irony, along with specific theories such as echoic mention theory, pretense theory, allusional pretense theory, and a unified theory. The limitations of these theories in capturing the nuances of irony, especially in written text, are also discussed.
How many studies on automatic irony detection are reviewed?
The coursework reviews seven different studies on automatic irony detection. Each study is broken down into its materials, methods, results, and discussion sections.
What are the specific studies on automatic irony detection that are discussed?
The studies reviewed include: "Yeah Right": Sarcasm Recognition for Spoken Dialogue Systems; Lexical Influences on the Perception of Sarcasm; Using LSA to detect Irony; Clues for Detecting Irony in User-Generated Contents; Detecting Ironic Intent in Creative Comparisons; Automatic Satire Detection: Are You Having a Laugh?; and Semi-Supervised Recognition of Sarcastic Sentences in Twitter and Amazon.
What are the challenges in automatically detecting irony?
The coursework highlights the significant challenges in computationally identifying ironic intent. These challenges stem from the reliance on context, world knowledge, and the subtle nuances of language that are difficult for computers to understand. The absence of tonal cues in written text further complicates the process.
What is the role of corpus studies in this coursework?
Corpus studies play a crucial role in the analysis and development of automatic irony detection methods. The coursework implicitly emphasizes the importance of using large datasets of text to train and evaluate these methods.
What is the relationship between irony and sarcasm explored in this coursework?
The coursework explores the relationship between irony and sarcasm, acknowledging the frequent overlap but also potentially highlighting any distinctions between these closely related concepts. The studies reviewed often focus on sarcasm detection as a subset of the broader problem of irony detection.
What are the key words associated with this coursework?
Key words include: Verbal irony, sarcasm, automatic irony detection, linguistic theories, Gricean maxims, computational linguistics, corpus studies, natural language processing, machine learning, context, intent.
What is the structure of the coursework?
The coursework is structured into four main chapters: an introduction, a chapter on linguistic theories of irony, a chapter reviewing studies on automatic irony detection, and a concluding chapter on future work. Each chapter provides a detailed summary of its contents.
- Citation du texte
- Michael Fell (Auteur), 2010, Verbal Irony: Theories and Automatic Detection, Munich, GRIN Verlag, https://www.grin.com/document/184355
