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Corpora are nowadays widely used in both Translation Studies and Metaphor Studies. Their use in each domain has developed separately from each other. This section aims to review the use of corpora in Metaphor Studies and Translation Studies and how they converge to serve the purposes of a study that deals with the translation of metaphors in a genre-specific and subject-specific study. The first part of this section (5.3.1) briefly outlines the main characteristics of corpora in metaphor studies; the second part (5.3.2.) underlines the pragmatic aspects of using corpora in translation studies. The third part (5.3.3) briefly describes the available procedures for searching corpora, their potential and drawbacks.

5.3.1 Corpus approaches to metaphor analysis

The use of corpus techniques in Metaphor Studies has developed as a response to problems with the cognitive approach to linguistic evidence (Deignan 2008b: 151). Deignan (ibid.) argues that from this perspective, metaphor study often relies on decontextualized examples and, as noted earlier, sometimes on invented data. She advocates the use of corpus linguistic techniques to ‘investigate the claims of conceptual metaphor theory

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through the examination of naturally-occurring linguistic metaphors’ (ibid.:155). Furthermore, corpus linguistics techniques have revealed aspects of metaphor that remain hidden in cognitive research such as the social, (con)textual, genre-related, cultural, ideological and dynamic dimensions of metaphor (ibid.). Most recent metaphor studies rely either on existing corpora such as the BNC for English, a subsection of corpora such as the BNC Baby or corpora compiled and designed by the researcher. These latter are smaller in size compared to the existing corpora. In Steen et al., (2010b), for example, metaphors are searched in specialised corpora, each of which is intended to be a representative corpus of a genre such as academic writing, pragmatic (newspapers) writing and others. Musolff (2004) reports on his corpus of political texts, and Koller (2004) uses a corpus of business texts to investigate metaphors. Koller and Semino (2009) and Semino and Koller (2009) use a corpus they specifically designed to investigate metaphor in political speeches in German and Italian respectively. Research about metaphor in specific genres is also conducted through corpus techniques. Skorcznska (2005), Scorczynska and Deignan (2006), Deignan et al., (2013), for instance, investigate how metaphors are used in two genres: business academic articles and business popular articles. Caballero (2003; 2013a) investigates metaphor usage in the genre of architecture articles. In addition, there are several corpus-based studies conducted across languages. For instance, Deignan and Potter (2004) investigated metaphorical and metonymical use of the words ‘mouth’, ‘nose’, ‘heart’ and ‘eye’ in English and Italian to test how successful is CMT in explaining these lexis cross-linguistically; Charteris-Black and Ennis (2001), and more recently Muelas Gil (2016) investigated the use of metaphors in English and Spanish financial discourse. Although the above mentioned cross-language studies are not conducted from a translation perspective, they provide evidence of the relevance of a corpus-based approach to a study of metaphor in translation.

5.3.2 Corpus approaches to Translation Studies

A corpus-based approach is widely used nowadays not only in the field of metaphor studies but also in the field of translation studies (Ahmed 2007; Ahmed and Rogers 2001; Baker 1993; 1996; 1998; Bowker 2001; Bowker and Pearson 2002; Braun 2006; Laviosa 1998; 2002; Olohan 2004; Schäffner 1998; Zanettin 2012).

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Whilst the use of corpora in linguistics goes back to the 60s (Laviosa 2002: 05), their use in translation studies is more recent. Baker (1993) first underlined the potential of electronic corpora, although manual corpus-based studies predated her proposal, in studying translation emphasising the role of such methods in unveiling the features of translated texts or what she calls “universals of translation” (Baker 1993: 243).

Baker (1996: 176-177) further argues that corpus techniques allow testing of “the kind of distinctive, universal features that have been proposed in the literature but never tested on a large scale such as simplification, explicitation and normalisation.

However, corpus techniques can be used not only in the study of the universals of translation but also to identify other features related to specific translation aspects such as the translation of metaphors and how it can be constrained by the cultural references embedded in them as it is demonstrated in this study.

While the term ‘corpus’ is used traditionally to refer to any collection of texts that are searchable manually, it is now established in corpus-based studies that ‘corpus’ refers to any electronic collection of texts that is searchable by means of special software tools (Deignan, 2005).

There are various types of corpora that can serve different research purposes such as the monolingual/bilingual or multilingual comparable or parallel corpora, general or specialised corpora; unidirectional or bidirectional corpora (see Baker 1993 1996; Bowker and Pearson 2002; Frankenberg-Garcia 2009b; Laviosa 2002; Olohan 2004, Zanettin 2012, 2013). Olohan (2004), for instance, draws the attention to the fact that the definition of each corpus differs slightly from one domain of research to another. In translation studies, ‘comparable corpora’ refer to an electronic collection of texts in different languages which are not translations of each other, whereas ‘parallel’ corpora refer to an electronic collection of texts and their translations (ibid.:24). However, the terms “parallel corpora” and “comparable corpora” are used interchangeably. In the current study, the term “parallel corpus” is used to refer to a collection of electronic texts and their translations.

From the point of view of Descriptive Translation Studies, Olohan (ibid.:10) claims that corpus-based approaches to translation studies can help understand and describe translation profiles and provide some basis to interpret results.

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Olohan (ibid.) and other researchers have described different ways for researching corpora in translation research. In the next section, the stress is put on procedures developed specifically to investigate metaphors in different corpora.

5.3.3 Procedures for searching metaphors in corpora

As seen in the previous section, there are different types of corpora that range from general to specific or specialised corpora. Corpora can also be classified as unilingual or multilingual corpora. This section is concerned with the different possibilities of investigating metaphors through corpora.

Stefanowitsch (2006) argues that retrieving conceptual metaphors from any data is a thorny task as “conceptual mappings are not linked to peculiar linguistic forms” (2006:2). He reviews the available strategies for extracting linguistic metaphors from a corpus and classifies them into the following strategies:

1. Searching manually: which limits the research to corpora which have a manageable size for manual searching;

2. Searching electronically for the source-domain vocabulary. In this case, the researcher makes assumptions about potential domains s/he is likely to find in the corpus and then establishes a list of lexical items that will be searched in the corpus; 3. Searching electronically for the target domain vocabulary. In this case, the

researcher seeks to establish a keyword list of the target domain investigated and concordance the keywords in the corpus in order to retrieve all instantiations of the searched words;

4. Searching for sentences containing lexical items from both source and target domain;

5. Searching for metaphors using ‘markers of metaphor’ (called here metaphor signals). Stefanowitsch (2006) proposes to use metaphor signals listed by Goatly (1997/2011) to identify and retrieve metaphors automatically from a corpus as seen earlier in Chapter 3;

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6. Using annotated corpora. Stefanowitsch (ibid.) reports that few researchers have explored the possibility of annotating corpora for semantic fields. Annotated corpora are claimed to be increasingly available especially for English language (Kimmel 2012).

Stefanowitsch (ibid.) maintains that one or more of these strategies can be combined to investigate corpora. Some researchers use an amalgamation of manual and automatic analysis such as Koller and Semino (2009) and Semino and Koller (2009) who analyse a sample of their corpus manually and then compare the results to a keyword list to find if they are keywords in the whole corpus.

Other researchers such as Philip (2008) proposes an automated procedure, instead of combining a manual and automatic search, applicable to specialised corpora and based on keyword lists and raw frequencies. Philip (2008:91) compares the corpus word frequency list with a reference corpus word frequency list in order to determine the keyword list. Unlike Stefanowitsch (2006), who refers to the use of an existing large corpus as a reference corpus, Philip (2008:100) claims that the use of a smaller reference corpus is more likely to reveal pertinent words in the case of specialised corpora. She further proposes that the list of low-frequency content words (LFCWs) “are where the metaphor vehicles and source domains will be found”. She stresses, however, that this is the case for Italian in particular.

A similar procedure is used by Rodriguez Marquez (2010) in her study of metaphors in financial reports between Mexican Spanish and American English. However, she uses a high-frequency word list instead of a low-frequency word list to search for metaphor candidates in her bidirectional bilingual corpus.

This section has revealed that while corpus techniques offer a high potential for metaphor identification, there are different ways of searching corpora. Each method may lead to different results. It is worth noting that whatever method is chosen, the manual analysis remains ineluctable even in the case of automated research as it is the only way to check the metaphoricity of the metaphor candidates initially identified in the corpus.

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This section reviewed different available procedures for searching metaphor in electronic corpora with a focus on specialised corpora. The next section reviews available methods for identifying what counts as a metaphor in the first place.

A number of researchers have proposed ways to define what counts as a metaphor in context. Charteris-Black (2004), for instance, argues that when the meaning of a single word is incongruent with the context, this indicates that the word might be used metaphorically. However, few researchers have proposed a detailed procedure to identify metaphors. The two main procedures available nowadays are the Metaphor Identification Procedure (MIP) and its newest version known as MIPVU. The next section reviews both procedures, highlights the advantages of using the MIPVU over the MIP and more importantly underlines the necessity to adjust the procedure to fit with the requirements of a bilingual corpus of English Arabic texts. The adjustments introduced to the identification method are covered in detail in the methodology chapter.