# Methods of Annotating and Identifying Metaphors in the Field of Natural Language Processing
## 1 Introduction  
Metaphors are an integral part of speech and enrich communication, but they also, as shown by [PERSON] and [PERSON] [1], form our opinion about the world and the phenomena that surround us. For instance, we often use the metaphorical concept TIME IS MONEY ([PERSON] and [PERSON] also introduced writing the concepts in capital letters, which is generally accepted in the literature dealing with (conceptual) metaphors). (e.g., \"You're _wasting_ my time.\") or LOVE IS A JOURNEY (e.g., \"This relationship is a _dead-end street.\"_) (Examples taken from [1]). These concepts allow us to map knowledge about one domain, which is physical and more basic (money, travel), to a domain that is more complex and abstract (time, love).  
Metaphors are used in speech and expressed with words, which makes them an area of interest in linguistic research, but they also represent a great challenge in the field of natural language processing. Precisely because they are an integral part of speech and of how we express ourselves, if we want to achieve further progress in natural language processing and artificial intelligence, metaphors are a phenomenon that needs to be studied in an interdisciplinary manner, so that we can understand their cognitive linguistic theory and recognize the reach of computational linguistics as well as machine and deep learning.  
In this context, this paper aims 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. This is done through firstly providing an outline of cognitive linguistic metaphor theory, which is given in the section following this introduction. The theoretical framework is then followed by presenting the approaches and procedures for metaphor annotation in text. In additionto giving an overview of the approaches and procedures, this section also shows how linguistic theories of metaphor are applied in the creation of data sets, which are then used in identifying metaphors in machine learning methods. Finally, the last section of the paper provides an overview of research on computer methods of metaphor identification, from approaches with hand-coded knowledge and lexical resources, to the latest state-of-the-art models that use neural networks and contextual word embeddings (i.e., large language models).  
The approach of the paper consists of systematic and chronological presentation of annotation methods and developments in identifying metaphors in the field of natural language processing. Acknowledging the fact that the field of natural language processing is vast and that new approaches are constantly (and simultaneously) being generated, this paper brings the results of the most relevant current research relating to metaphor identification in one place. Therefore, its ambition is not to decide on the success of the methods/models presented but rather to map the field and present the current state of play. The ever-growing tendency which characterizes this field makes it difficult for researchers to grasp the developments without investing significant time. With providing a systematic and comprehensive overview, this paper serves as an introduction to the field and presents a source of information for a broad spectrum of researchers who (are starting to) work in the field of natural language processing.