Figurative language: The specific case of irony detection
Figurative language is one of the most arduous topics facing natural language processing. Unlike literal language, the former takes advantage of linguistic devices, such as metaphor, analogy, ambiguity, irony, sarcasm, satire and so on, in order to project more complex meanings which, most of times, represent a real challenge, not only for computers, but for humans as well. This is the case of humour and irony. Each device exploits different linguistic strategies to be able to produce an effect (e.g., ambiguity and alliteration regarding humour; similes regarding irony). Sometimes the strategies are similar (e.g., use of satirical or sarcastic utterances to express a negative attitude). These devices suppose cognitive capabilities to abstract and meta-represent meanings out of the "physical" words. In this communicative layer, communication is more than sharing a common code, but being capable to infer information beyond syntax or semantics; i.e., figurative language implies information not grammatically expressed to be able to decode its underlying meaning: if this information is not unveiled, the real meaning is not accomplished and the figurative effect is lost.
This kind of information supposes a great challenge because it points to social and cognitive layers quite difficult to be computationally represented. However, despite the inconveniences that figurative language supposes, the approaches to automatically process figurative devices, such as humour, irony or sarcasm, seem to be largely encouraging.
In this framework, in this talk we will aim at showing how two specific domains of figurative language - humour and irony - may be automatically handled by means of considering linguistic devices, such as ambiguity and incongruity, and meta-linguistic devices, such as emotional scenarios and polarity (in irony a polarity negation phenomenon occurs). We especially focus on discussing how underlying knowledge, which relies on shallow and deep linguistic layers, may represent relevant information to automatically identify figurative usages of languages. In particular, and contrary to the most of the researches which deal with figurative language, we aim at identifying figurative usages regarding language in social media. This means that we do not focus on analyzing prototypical jokes nor literary examples of irony, rather, we try to find patterns in texts whose intrinsic characteristics are quite different to the ones described in the specialized literature. For instance, a joke which exploits phonetic devices to produce a funny effect, and a tweet in which humour is self-contained in the situation. Considering this scenario, we suggest a set of features which work together as a system: no single feature is particularly humorous or ironic, but all together provide a useful linguistic inventory for detecting humour and irony at textual level.
Paolo Rosso is an associate professor at the Natural Language Engineering Lab of the Universidad Politécnica de Valencia). He presents joint work with Antonio Reyes, a PhD student at his lab working on irony detection. Among Paolo Rosso's main research interests are plagiarism detection, irony detection, sentiment analysis, and automatic humour recognition.
This colloquium is sponsored by CLIF, the scientific research community for Computational Linguistics and Language Technology.
The colloquium takes place in Annexe, Lange Winkelstraat, 2000 Antwerp (building 3 on the campus map)