A Unified Theory of Irony and Its Computational Formalization
Summary
Computational linguistics had largely ignored irony, partly because older accounts treated it as simply saying the opposite of what is meant. Utsumi argues that earlier theories, including Gricean, echoic/mention, and allusional pretense theories, blur two separate questions: what makes an utterance ironic, and how a hearer recognizes it as ironic. He proposes a unified theory built on the notion of an "ironic environment." In this situation, a speaker held an expectation, the expectation failed, and the speaker now feels negatively about the gap between hope and reality.
An ironic utterance conveys this environment indirectly through three components:
- alluding to the expectation,
- deliberately violating a pragmatic principle (pragmatic insincerity),
- hinting at the speaker's emotional attitude.
Hearers need to detect only two of the three components to read an utterance as ironic. The environment and its implicit display are formalized with situation theory (infons, supporting situations) combined with situation calculus and action theory. The paper offers no corpus or experiments. Its contribution is a framework precise enough to underpin NLP systems that interpret or generate irony.
Key points
- An ironic environment is defined by three conditions over two time points t0 < t1. The speaker holds expectation E at t0, E has failed by t1, and the speaker has a negative attitude toward the resulting incongruity.
- There are four environment types, depending on whether E stems from an intentional action A and on how E failed. These types are used to predict who becomes the victim of irony.
- The "2-of-3" recognition criterion explains unintentional irony and the lack of a need for special intonational cues. Both findings are consistent with psycholinguistic results by Gibbs and O'Brien (1991).
- Representation relies on Barwise's (1989) situation theory plus situation calculus, illustrated on five worked examples (e.g., the eaten pizza, the messy room).
- Sarcasm is distinguished from irony by an explicit victim and a display of counterfactual pleased emotion. Sarcasm without allusion to an expectation counts as non-ironic sarcasm.
Cited by
- An Impact Analysis of Features in a Classification Approach to Irony Detection in Product Reviews
- Multilingual Irony Detection with Dependency Syntax and Neural Models
- Semi-Supervised Recognition of Sarcasm in Twitter and Amazon
- Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing
- Exploring the Realization of Irony in Twitter Data
- Towards a Contextual Pragmatic Model to Detect Irony in Tweets
- Mining Subjective Knowledge from Customer Reviews: A Specific Case of Irony Detection
Cite
@inproceedings{utsumi1996unified,
title = {{A Unified Theory of Irony and Its Computational Formalization}},
author = {Utsumi, Akira},
booktitle = {COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics},
year = {1996},
doi = {10.3115/993268.993334},
url = {https://doi.org/10.3115/993268.993334}
}References
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