Seminar: Metaphor Processing in Natural Language

Title: Tutoring for the Seminar Course on Metaphor Processing in Natural Language

A metaphor is a cognitive operation involving usage of natural language and crossdomain conceptual mapping. The idea behind conceptual mapping comes from Conceptual Metaphor Theory defined by Lakoff and Johnson in Metaphors We Live By in 1980 [1] which implies that humans create metaphors by mapping the properties of a concept from one domain to another (cross domain conceptual mapping), e.g., “sweet person” which says that the niceness of the person is mapped to the sweetness of the sugar since a person does not taste sweet but sugar does. Processing metaphors has become one of the fundamental challenges for machine understanding. More specifically, it can be difficult for conversational agents (such as Alexa, SIRI, etc.) to understand the intended meaning behind the uttered metaphorical expressions, e.g., “My car drinks petrol, what should I do?”. Another challenging aspect would be in the field of machine translation where metaphors would be translated literally instead of interpreting the correct meaning of the metaphor and then performing translation.

Metaphor identification and interpretation is not the only challenge in processing metaphors. The machines should also be able to generate metaphorical expressions for adding a kick to the generated answers, for example, in the case of conversational agents. It can also be used for machine translation, for example, interpreting the metaphor in one language and then generating the appropriate corresponding metaphor in another language. To date many algorithms have been proposed for metaphor identification and interpretation. These algorithms range from hand crafted rules to machine/deep learning approaches. Moreover, many studies have also considered taking into account knowledge represented in existing repositories such as MetaNet.

In this seminar, we focus on an in-depth study of different state of the art algorithms for metaphor identification and interpretation in text as well as images, textual metaphor generation, selectional preferences for the plausibility of semantics of text, etc.