Methods of Annotating and Identifying Metaphors in the Field of Natural Language Processing

Abstract

Metaphors are an integral and important part of human communication and greatly impact the way our thinking is formed and how we understand the world. The theory of the conceptual metaphor has shifted the focus of research from words to thinking, and also influenced research of the linguistic metaphor, which deals with the issue of how metaphors are expressed in language or speech. With the development of natural language processing over the past few decades, new methods and approaches to metaphor identification have been developed. The aim of the paper is to map the methods of annotating and identifying metaphors in the field of natural language processing and to give a systematic overview of how relevant linguistic theories and natural language processing intersect. The paper provides an outline of cognitive linguistic metaphor theory and an overview of relevant methods of annotating linguistic and conceptual metaphors as well as publicly available datasets. Identification methods are presented chronologically, from early approaches and hand-coded knowledge to statistical methods of machine learning and contemporary methods of using neural networks and contextual word embeddings.