Publishing Partner: Cambridge University Press CUP Extra Publisher Login

New from Cambridge University Press!


Revitalizing Endangered Languages

Edited by Justyna Olko & Julia Sallabank

Revitalizing Endangered Languages "This guidebook provides ideas and strategies, as well as some background, to help with the effective revitalization of endangered languages. It covers a broad scope of themes including effective planning, benefits, wellbeing, economic aspects, attitudes and ideologies."

New from Wiley!


We Have a New Site!

With the help of your donations we have been making good progress on designing and launching our new website! Check it out at!
***We are still in our beta stages for the new site--if you have any feedback, be sure to let us know at***

Review of  Metaphor

Reviewer: Pia Sommerauer
Book Title: Metaphor
Book Author: Tony Veale Ekaterina Shutova Beata Beigman Klebanov
Publisher: Morgan & Claypool Publishers
Linguistic Field(s): Computational Linguistics
Cognitive Science
Issue Number: 29.3264

Discuss this Review
Help on Posting

Despite their central importance for many domains of language, metaphor and other kinds of figurative language have, until rather recently, received comparatively little attention from the computational world. The book ‘A computational perspective’ summarizes recent developments in Natural Language Processing (NLP) as well as theoretical accounts from long-standing traditions in fields like philosophy, psychology and linguistics in order to introduce metaphor to NLP researchers as well as interested scholars from other fields.

The first chapter, ‘Introducing metaphor’ first and foremost provides the reader with a feeling for metaphor on the level of language, but also on a deeper, conceptual level. The idea of conceptual metaphors as mapping between a source and a target domain is illustrated by well-chosen examples. It is shown that metaphor and other instances of figurative language are by no means easy to model in all their aspects and variations and pose highly complex problems to computational approaches. The chapter ends with situating the problems within philosophy, psychology and cognitive sciences on the one hand, where they have received considerable theoretical attention, and computational linguistics, in particular NLP, on the other hand, which has shied away from its complexities for a long time. It is argued that despite the challenges involved, the area is particularly suitable to designing cognitive models that are “practical and efficient and cognitively plausible” (p. 3).

Chapter 2, ‘Computational approaches to metaphor: Theoretical Foundations’, aims at providing an overview of the most important theoretical approaches to metaphor and other phenomena of figurative language. It opens with raising the questions of how metaphor can be defined, why it is used and what its underlying mechanisms are. In the course of the chapter, aspects of metaphor interpretation (the ‘meaning’ of metaphor) are discussed from different semantic perspectives, before examining closely related phenomena of figurative speech such as simile and analogy and their relation to metaphor. One central idea introduced about metaphorical language is that it ideally functions as a conceptual pact between the participants of a conversation (Brennan & Clark, 1996), relying on a variety of cultural factors. After explaining the fundamental assumptions of Conceptual Metaphor Theory (Lakoff & Johnson 1980) and Conceptual Integration Theory (Fauconnier & Turner, 2008), the concepts introduced in the chapter are integrated in the final section, which argues that rather than constituting different phenomena with different underlying mechanisms, they can be placed on a “continuum of integration” (p. 30) ranging from loose comparisons invited by similes to tightly integrated conceptual metaphors and blends.

Following these theoretical foundations, Chapter 3, ‘Artificial Intelligence and Metaphor’, outlines three major ways of approaching metaphor from a computational modelling perspective. The first approach frames the problem of metaphor detection and interpretation as a correction-problem in which non-literal instances should be recognized as ‘violations’ and translated into literal equivalents. These systems mainly operate with hard-coded semantic knowledge in the form of selectional preferences or semantic features. Alternatively, the problem can be approached from the perspective of mapping structures to find analogies between source and target concepts. Such analogical approaches crucially depend on theoretical assumptions and design choices of knowledge representation. The third approach introduced in this chapter operates on the basis of schemas representing general metaphorical mappings. These mappings are used to detect instances of deeper, conceptual metaphors. This third approach can be seen as the closest implementation of Conceptual Metaphor Theory and, in contrast to the other approaches, makes the encodings of metaphors explicit. The chapter closes with a reflection on the aspects shared by all three approaches and potential future work in the direction of computational models of conceptual integration.

The main purpose of Chapter 4, ‘Metaphor Annotation’, is to give a comprehensive overview of ways of approaching metaphor annotation and available annotated data sets. A particular focus is placed on the Metaphor Identification Procedure (MIP) suggested by the Pragglejaz Group (2007) and its variants, in particular the MIPVU (Steen et al. 2010) developed in the course of annotating the VUAmsterdam corpus. This procedure stands out as it is the first general approach to metaphor identification independent of genre or text type. Several difficult but crucial aspects of annotation guidelines that can have significant impact on the result of the annotation procedures are discussed, such as what extent annotators should rely on external resources or their own intuition. The chapter closes with a comprehensive overview and comparison of available data sets.

While Chapter 3 outlines how computational models of metaphor can employ knowledge to detect and interpret metaphors, Chapter 5, ‘Knowledge Acquisition and Metaphor’, provides an account of how this knowledge can be acquired. Structured knowledge bases, as well as the vast amount of naturally occurring tests found on the Web and complementary approaches exploiting the strengths and weaknesses of each type of resources, are introduced. Particular attention is given to ways in which pattern searches inspired by Hearst patterns (Hearst 1992) can be combined to extract knowledge that is rarely expressed explicitly in a straightforward way but constitutes an important component in figurative expressions, such as common stereotypes.

Statistical methods, in particular models based on the distribution words have in large corpora have recently found a wide application in many NLP tasks, one of which is the analysis of metaphor. Chapter 6, ‘Statistical approaches to metaphor’, reviews a number of such statistical methods, which have the advantage of being largely independent of lexical resources, leading to potentially higher coverage and better portability. Statistical information can for instance be used to determine selectional preferences but faces similar challenges when faced with conventionalized metaphors. Several approaches based on clustering are introduced, in which a space of words based on linguistic features is partitioned into groups with similar words. The resulting structure can be exploited to find, for instance, words from different domains which are metaphorically associated with the same source domain. The chapter also outlines approaches that build on the idea of topical structures that are ‘interrupted’ by words from a metaphorically used source domain and briefly outlines approaches based on vector space models, whose information about lexico-syntactic contexts is exploited for paraphrasing approaches and source-domain assignment. The final section summarizes approaches exploiting information about concreteness by, for instance, finding pairs of abstract and concrete expressions.

Following these outlines of major computational approaches and methodologies, Chapter 7, ‘Applications of Metaphor Processing’, introduces several interesting directions in which automatic tools can benefit from metaphor processing. First and foremost, it is stressed that metaphors can yield insights into the way different linguistic communities conceptualize the world. Furthermore, the ability to use and comprehend metaphors can provide indications about second language proficiency. These areas of application can benefit substantially from automatic metaphor processing tools. Another direction is applying metaphor processing to tools supporting creative tasks. In particular, metaphor generation tools could provide support for writing tasks. An example of how automatically retrieved mappings could be used to create an entire narrative structure is provided.

The concluding chapter highlights the many ways in which metaphor can be approached and interpreted, depending on a variety of contextual factors. Most importantly, however, it underlines the importance of metaphorical phenomena for NLP, as they affect virtually any task. Hence, robust and available and accessible tools offering metaphor processing is what the field should aim for.


The main goal of this book is “to be a comprehensive guide to the major landmarks in the computational treatment of metaphor” (p. 4) for an audience ranging from beginning to experienced researchers in the NLP community. Without doubt, this goal is achieved as the book treats a multitude of recent approaches from all major perspectives on NLP and artificial intelligence and contains an introduction to major theoretical assumptions and the role of metaphor processing and generation in applications. Furthermore, the book makes a convincing case for the central role metaphor plays in many tasks that involve semantic interpretations, making clear why metaphor should no longer play a marginal role in NLP. Beyond this, the book has several other strengths.

First, the book provides a vast number of truly illustrative examples that not only exemplify linguistic phenomena and help the reader grasp subtle differences, but also invite the reader to to think a step further and ‘play’ with metaphorical mappings. The introductory chapter in particular outlines the most important phenomena in a seemingly effortless way by means of a number of very clear examples. The subsequent chapters continue to supply a range of such illustrations (often taken from well-known media discourse), which might be of particular help for readers less familiar with linguistic theories.

A second strong point is the comprehensive overview of data sets annotated for figurative language use provided in Chapter 4. In particular, the table presented at the end offers a highly useful and well-organized overview or resources encompassing, among others, information about the language, text type of the source data, size, inter-annotator agreement and type of annotations. This is of great help for researchers in the field as well as related fields looking for resources.

Third, the book employs a number of effective figures that are easy to understand and help to clarify complex theoretical ideas, such as Conceptual Integration or or rather complex computational approaches, such as statistical methods to metaphor processing.

An aspect that could cause potential difficulties for the reader is that in some instances, the relation to other parts of the book is not entirely clear or arises rather implicitly. Up to a certain extent, this is not the fault of the authors, but due to the nature of the field, which has developed its theoretical assumptions in philosophy, psychology and linguistics, whereas the major computational models and implementations have been developed within the rather engineering-driven field of NLP. For rather inexperienced readers, this might render it somewhat difficult to connect and compare the different approaches. Starting researchers may find this challenging. Unfortunately, the rather dense layout does not always support the reader in finding their way through the book and sectioning is not employed consistently.

In conclusion, the book constitutes a highly useful summary of approaches, with chapters on theoretical assumptions, artificial intelligence perspectives, annotation and data sets, knowledge retrieval, statistical approaches and applications that also lend themselves well to be read individually and yield valuable explanations as well as practically useful overviews of data sets and approaches. It makes a convincing case for the central role of metaphor for a wide variety of semantic phenomena and in particular, for NLP tasks and encourages further research in a newly established field.


Brennan, S.E. and Clark, H.H. 1996. Conceptual pacts and lexical choice in conversation. Journal of Experimental Psychology: Learning, Memory, and Cognition 22(6), p. 1482.

Fauconnier, G. and Turner, M. 2008. The way we think: Conceptual blending and the mind’s hidden complexities. Basic Books.

Group, P. 2007. MIP: A method for identifying metaphorically used words in discourse. Metaphor and symbol 22(1), pp. 1–39.

Hearst, M.A. 1992. Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th conference on Computational linguistics-Volume 2. Proceedings of the 14th conference on Computational linguistics-Volume 2. Association for Computational Linguistics, pp. 539–545.

Lakoff, G. and Johnson, M. 1980. Metaphors we live by Chicago. Chicago University

Steen, G. 2010. A Method for Linguistic Metaphor Identification : From MIP to MIPVU. John Benjamins Publishing Co.
Pia Sommerauer is currently a PhD student at the Computational Lexicology and Terminology Lab Amsterdam at Vrije Universiteit Amsterdam and the Metaphor Lab Amsterdam. She completed her Master's degree in linguistics with a specialization in Human Language Technology at Vrije Universiteit Amsterdam. Her research focuses on the way ambiguity involved in the semantic phenomena sense and reference is represented in distributional semantic models.

Format: Electronic
ISBN-13: 9781627058513
Pages: 160
Prices: U.S. $ 44
Format: Paperback
ISBN-13: 9781627058506
Pages: 160
Prices: U.S. $ 55