Using Figurative Language and Other Co-textual Markers for the Automatic Classification of Irony

Sarcasm;Irony;Hyperbole;Understatement;Figurative Language;401

Andrew Cattle인문대학 언어학과 ;

University:서울대학교 대학원

关键词: Sarcasm;    Irony;    Hyperbole;    Understatement;    Figurative Language;    401;   

Others  :  http://s-space.snu.ac.kr/bitstream/10371/131945/1/000000021333.pdf

美国|英语

来源: Seoul National University Open Repository

【 摘 要 】

This thesis proposes a linguistic-based irony detection method which uses these frequently co-occurring figurative languages to identify areas where irony is likely to occur. The detection and proper interpretation of irony and other figurative languages represents an important area of research for Computational Linguistics. Since figurative languages typically convey meanings which differ from their literal interpretations, interpreting such utterances at face value is likely to give incorrect results. Irony in particular represents a special challenge as, unlike some figurative languages like hyperbole or understatement which express sentiments which are more-or-less in line with their literal interpretation, differing only in intensity, ironic utterances convey intended meanings incongruent with – or even the exact opposite of – their literal interpretation. Compounding the need for effective irony detection is irony’s near ubiquitous use in online writings and computer-mediated communications, both of which are commonly used in Computational Linguistics experiments.While irony in spoken contexts tends to be denoted using prosody, irony in written contexts is much harder to detect. One of the major difficulties is that irony typically does not present with any explicit clues such as punctuation marks or verbal inflections. Instead, irony tends to be denoted using paralinguistic, contextual, or pragmatic cues. Among these are the co-occurrence of figurative languages such as hyperbole, understatement, rhetorical questions, tag questions, or other ironic utterances which alert the listener that the speaker does not expect to be interpreted literally.This thesis introduces a divide-and-conquer approach to irony detection where co-occurring figurative languages are identified independently and then fed into an overall irony detector. Experiments on both short-form Twitter tweets and longer-form Amazon product reviews show not only that co-textual figurative languages are useful in the automatic classification of irony but that identifying these co-occurring figurative languages separately yields better overall irony detection by resolving conflicts between conflicting features, such as those for hyperbole and understatement.This thesis also introduces detection methods for hyperbole and understatement in general contexts by adapting existing approaches to irony detection. Before this point hyperbole detection was focused only on specialized contexts while understatement detection had been largely ignored. Experiments show that these proposed automated hyperbole and understatement detection methods outperformed methods which rely on fixed vocabularies.