From the explanation offered so far, it is perhaps evident that some of the reasons for adopting this formalism have become obvious. The choice of CGs is informed by the many reading research findings, some of which stipulate that making connections is a major hallmark of successful readers (Fisher & Frey 2008:16ff;). Keene and Zimmerman (2007) reveal that good readers make different categories of connections with the text when they activate their prior knowledge stored in the long-term memory as frames, schemata, ICM, mental spaces or scripts. Rojo and Ibarretxe-Antuñano (2013:15) also reveal that linguistic analysis involves the exploitation of mental faculties, such as memory, attention and reasoning in the investigation
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of meaning at all levels, a proposal credited to Lakoff and referred to as “cognitive commitment” (Lakoff 2006:234). Furthermore, according to Fillmore (2006:383) the process of understanding a text involves retrieving or perceiving the frames evoked by the text’s lexical content and assembling this kind of schematic knowledge into some sort of personal perception of the text. The importance of this fact is that the graphs, which outline entities and specify their relationships, make it easier for the reader to decide which aspect of the message requires a focus of attention. Considering the fact that language is just a vehicle for expressing thought and that such thoughts, in the source language, may be conveyed via too many linguistic constructions, the graph therefore provides a framework upon which the relevant conceptual structure may be selected and reconstructed in the target language using less or more linguistic constructions.
Explaining the complex cognitive processes involved in translation is perhaps the most fundamental and yet difficult challenge that a cognitive description has to face. Alexander Ziem’s (2014:219) assertion that schemata activation allows semantic relations to be established between linguistic expressions may hold one of the keys to this explanation since relations play a significant role in the production of a more comprehensible TT. More often, the major cause of comprehension problems among readers is their inability to establish connections between one part of the text and another (Mandler 2014:10). Better application of CGs could however provide a certain number of stimuli towards establishing relations between one entity and another or among a group of entities. Bunescu and Mooney (2007:29) observe that extracting semantic relationships between entities mentioned in a text is an important task in natural-language processing. They reveal that information extraction from newspaper articles is usually concerned with identifying allusions to people, organisations, locations, and extracting useful relations between them. Relevant relation types range from social relationships, to roles that people hold inside an organisation, to relations between organisations, to physical locations of people and organisations. In certain cases, entities in a text and their relations are far from one another and become difficult to retain in the short-term memory of the reader.
As an example to the above account, one of the French newspaper reports used for the present pilot study (see Appendix 2) mentions in the first paragraph a particular individual (Henry Proglio) and the position he occupies. The second paragraph cites another organisation to which he is affiliated and announces two other outfits related to the organisation. In addition, the third paragraph specifies the nature of his affiliation with the company and concludes with
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several choices he made in relation to the company. The range of information about the person scattered through all the paragraphs of the text was confusing to some of the students. From a cognitive psychological perspective, this array of information weighed heavily on the readers’ cognitive processing to such an extent that they were not able to simultaneously process all in the short-term memory. Since student translators are said to be in the process of acquiring expertise and do not always have adequate processing capacity to handle all the textual contents at the same time, the following graph provided some assistance.
Figure 3.4: More extended conceptual graph
Figure 3.4 contains the CGs representing the newspaper report described earlier. The graphs bring together some of the key information about Proglio, the principal character of the text. For translation, the comprehension facilitated by the graphs could inspire a TT recreation that explicates some of the information and makes it easier for the TT reader to comprehend. In spite of several scholarly protests (cf. the volume edited by Mauranen and Kujamäki [2004] and the critique by Pym [2008:317]) against the translation universal identified by Baker (1993), there seems to be, in recent years, some empirical evidence in support of the tendency for translators to make their TTs more comprehensible (Hansen-Schirra & Gutermuth 2015). Although comprehensibility has to do with both the source and the TTs, it is said that at the core of every translation activity there is a problem of comprehensibility (Maksymski 2015:13). The following paragraph explores this a little further.
According to Maksymski (2015:11), comprehensibility can relate to the translator and the ST, especially when the translator does not understand the meaning of the ST or misinterprets it. From the standpoint of research findings that translators are special readers, one could reasonably conclude that any ST that is difficult for a translator may be difficult for several
Former Director EDF Chairperson BOD Rosatom Henry Proglio Fennovoima LTD Akkuyu Nuclear JSC branch aspiring position headship of Thales position
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other readers4. Since the translator him/herself may very well become a victim of text difficulty, Jensen (2015) has proposed that an intralingual approach to creating optimum comprehensibility be adopted. According to Jensen, functional texts, such as instruction manuals for consumer goods, patient information leaflets on medication, informed consent forms in clinical trials and tax information brochures — although primarily written for the general public — are produced by subject specialists. These experts sometimes may be influenced by the writing styles of their various professions, which might render the text more or less clumsy and difficult to understand. Jensen argues that translators, in some cases, go the extra mile to rewrite or rephrase those texts in the original language in order to produce a more comprehensible TT. Along this same rephrasing dimension, Hasegawa, Ohara, Lee-Goldman and Fillmore (2006) provided more insight into the role of rephrasing a text segment in order to produce a more idiomatic translation. In a careful survey of sentences expressing the various concepts that make up the family of risk frames as described for English, the authors found clear cases illustrating differences in basic clause structure between English and Japanese. These differences suggest preferences for one way or another of selecting ‘head’ and ‘subordinator’ between the expression of the risk-taking action and the concept of risk itself. In the case of daring risk, the possibility of expressing the risk concept as clausal head does not exist in Japanese. A possible way out would require, among other things, to rephrase the language of the original by head-switching, that is, a form of predicate alternation (Hasegawa, Ohara, Lee-Goldman & Fillmore 2006:2).
Consequently, since, as indicated above, CGs favour the rewording of a text in several alternative versions, its use might provide some assistance for text comprehension in translation. Yang and Soo (2012:876) have shown that CGs are a useful tool for the extraction of relevant information from patent claims. Normally, a patent claim contains two types of information — structural information and methodology information. Structural information considers what physical entities are used and how they are composed and connected in a patent claim while the methodology information considers how the processes and functions are conducted in order to achieve a certain design purpose or goal. Yang and Soo (2012:875) demonstrate the large number of technical domain terms and lengthy sentences prevalent in patent claims. This information, according to the scholars, are inherently difficult and time- consuming to parse using the generative grammatical method of sentence analysis, especially
4 If another reader with similar language comprehension skills and competence does not find the same text difficult, my assumption is that the nature of attention required for the production of a second text from the origical is not being invested.
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when they need to be urgently extracted in the cooperate sector to meet relevant needs. Yang and Soo’s study therefore combines other sentence-tagging techniques and CGs to convert a patent claim into a formally defined conceptual graph. An evaluation of contents based on the same formal criteria reveals that out of 100 patent claims, there were an average precision and recall of a concept-class mapping from the patent claim to domain ontology of 96% and 89% respectively. Similarly, the average precision and recall for real relation-class mapping were 97% and 98% respectively. For the concept linking of a relation, the average precision was 79%. The basic interpretation of these results is that extracting CGs from patents would facilitate automated comparison and summarisation among patents for quick judgment of patent infringement.
The parsing difficulty that results from the habitually lengthy sentences found in some documents, such as patent claims, clearly demonstrates the limitations of the traditional grammatical method of analysis. More appropriate is the intervention of CGs formalism, especially in keeping track of the several discourse referents that would otherwise have proved difficult in tracking. Representing such complex sentence structures would facilitate their being split into manageable forms without compromising the sense of the original construction. This is in line with several research findings and stipulations in a number of comprehensibility criteria that shorter and simpler sentences are easier comprehensible than more complex ones (Wolfer, Hansen-Morath & Konieczny 2015:264). Related to the simplification potential of the CGs is the advantage which knowledge of the principles of cognitive linguistics brings to the translation of these documents. One such fundamental view about language is that grammatical constructions cannot be considered independent of their meanings in real life. In both the reading of the text and its subsequent transfer process, each linguistic element or construction is seen as a building block to our comprehension of the relationship between what is already known and the new information being read on the page.
Because of the role that identifying these (textual) relations plays in understanding a text (Risko, Walker-Dalhouse, Bridges & Wilson 2011:376), several approaches have emerged to provide easy access to the information in the text. We refer to textual features such as these as ‘cohesive elements’, and they occur within paragraphs (locally), across paragraphs (globally) and in referential, causal, temporal and structural forms. But cohesive elements, and thus cohesion, does not simply feature in a text as dialogues tend to feature in narratives, or as cartoons tend to feature in newspapers. That is, cohesion is not present or absent in a binary or optional sense. Instead, cohesion in text exists on a continuum of presence, which is sometimes
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indicative of the text-type in question and sometimes indicative of the audience for which the text was written (McCarthy, Briner, Rus & McNamara 2007). Cohesion is the degree to which ideas in the text are explicitly related to each other and facilitate a unified situation model for the reader. Certain cohesive ties are not made explicit in the text and would require a conscious effort to identify them. Just like visual images facilitate the processing of meaning (see the review by Orenes & Santamaría 2014, for example), CGs can more convincingly be seen as an instance of visualisation. The following section considers the roles of visualisation in meaning construction – text comprehension, in this case.