Other examples of common metaphorical mappings include: time is money (e.g., “That flat tire cost me an hour”) ideas are physical objects (e.g., “I can not grasp his way of thinking”) violence is fire (e.g., “violence flares amid curfew”) emotions are vehicles (e.g., “ she was transported with pleasure”) feelings are liquids (e.g., “ all of this stirred an unfathomable excitement in her”) life is a journey (e.g., “He arrived at the end of his life with very little emotional baggage”). They coined the term conceptual metaphor to describe the mapping between the target concept (e.g., politics) and the source concept (e.g., mechanism), and linguistic metaphor to describe the resulting metaphorical expressions. Lakoff and Johnson claimed that metaphor is not merely a property of language, but rather a cognitive mechanism that structures our conceptual system in a certain way. The most influential of these was the Conceptual Metaphor Theory of Lakoff and Johnson ( 1980). The view of metaphor as a mapping between two distinct domains was echoed by numerous theories in the field (Black 1962) Hesse 1966 Lakoff and Johnson 1980 Gentner 1983). As a result, we reason about political systems in terms of mechanisms and discuss them using the mechanism terminology in a variety of metaphorical expressions. The existence of this association allows us to transfer knowledge and inferences from the domain of mechanisms to that of political systems. For instance, when we talk about “the turning wheels of a political regime,” “ rebuilding the campaign machinery” or “ mending foreign policy,” we view politics and political systems in terms of mechanisms-they can function, break, be mended, have wheels, and so forth. Metaphors arise from systematic associations between distinct, and seemingly unrelated, concepts. At the same time, it plays an important structural role in our cognition, helping us to organize and project knowledge (Lakoff and Johnson 1980 Feldman 2006) and guide our reasoning (Thibodeau and Boroditsky 2011). Metaphor brings vividness, distinction, and clarity to our thought and communication. Finally, we demonstrate that statistical methods can facilitate and scale up cross-linguistic research on metaphor. In order to investigate the scalability and adaptability of our models, we applied them to data in three languages from different language groups-English, Spanish, and Russian-achieving state-of-the-art results with little supervision.
Our aim is to identify the optimal type of supervision for a learning algorithm that discovers patterns of metaphorical association from text. learning without annotation) in flat and hierarchical, unconstrained and constrained clustering settings. learning from a given set of metaphorical mappings vs.
We investigate different levels and types of supervision (learning from linguistic examples vs. In contrast, we experiment with weakly supervised and unsupervised techniques-with little or no annotation-to generalize higher-level mechanisms of metaphor from distributional properties of concepts. However, these approaches often require extensive human annotation effort and are predominantly evaluated within a limited domain. Recent years have witnessed a rise in statistical approaches to metaphor processing. Computational work on metaphor has traditionally evolved around the use of hand-coded knowledge, making the systems hard to scale. Highly frequent in language and communication, metaphor represents a significant challenge for Natural Language Processing (NLP) applications.