Thus the stylistic process, examining the creativity of language use, develops our understanding of literature. This can be done by expanding the literary intuitiveness of linguists and by expanding the linguistic observation and knowledge of critics. Thus, the linguists' attention will be focused on how the literary text represents the language system.
The critics will also consider the underlying significance of the text. The efforts of stylisticians to make such connections help develop our understanding of literature and of the underlying significance of texts. Stylistics uses many key terms and devices in its analysis of texts. However, we will be dealing with the most important ones used in the stylistic analysis. They are:. Alliteration means the repetition of the same consonant sounds in close words.
The consonant sounds must be in the initial position of words in order to be examples of alliteration. Alliteration possesses a cohesive effect due to the fact that similar sounds connect words together if they are in proximity. Alliteration is widely used in poetry, newspaper headlines, advertising, etc. Arbitrariness is one of the features of human language. It means that there is no recognizable connections between the sound signal and the message conveyed by human beings.
So, the word elephant, for instance, has no connections with the animal it symbolizes. They are, in this sense, similar to what they symbolize because of a certain facets of iconicity. Is the opposite of Alliteration. It means the repetition of the same vowel sound in close words. Just like alliteration, assonance possesses a cohesive effect due to the fact that similar sounds connect words together if they are in proximity. Assonance is widely used in poetry, newspaper headlines, advertising, etc. Conceptual blending theory is derived from cognitive linguistic traditions, conceptual metaphor theory and mental spaces theory.
It proposes that the construction of meaning doesn't rely only on pre-exsiting knowledge scope but also on temporary knowledge structure. Key questions about stylistics. A beginner's perspective Term Paper, 13 Pages, Grade: Ahmed M.
Hashim Author. Add to cart. Table of Content 1. Words that occur above a certain frequency will appear in a list of collocates, and some programs will allow the user to switch from the collocates list to show a concordance of all the lines in which the collocates appear with the search term. This may be done in order to reduce the number, if there are too many to analyse, or because the analyst is only interested in a particular subset. Ways of thinning concordance lines include reducing to the set to every nth occurrence, to n per text, or to the first n examples where n is any positive integer.
A set of concordance lines may also be thinned on the basis of user annotations see section 9 below. Searching in results to produce a reduced number of concordance lines see section 7 above can be one way of thinning the concordance lines.
Some programs allow the user to search for a string in the concordance lines, and then thin the set of concordances to only those which contain the search string. Figure 8: the concordance from figure 6 sorted on right co-text , thinned to display only every 5th occurrence. Analysis with this function Thinning lines is often part of the heuristic process of focussing the analysis on a particular area of usage in the corpus.
A corpus-driven enquiry will typically start with a search for a particular form, followed by analysis of its meaning and contexts, and then searching for a longer phrase. Thinning concordance lines is used chiefly for providing an appropriate number of examples for a human analyst to be able to view. This may be done for use in the classroom, so as not to swamp or intimidate the student with two many examples. Manually thinning lines is also possible, and may be useful for illustrative or pedagogic purposes, but there is a danger of making a biased selection, and it is important that the person reading the concordance knows that the lines have been manually selected.
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If it is intended to generalize from the analysis of the sample, then it is necessary to be aware of the way in which the corpus is structured, and to decide whether the sample is likely to be representative of all the examples. In a similar fashion, if the intention is to generalise about the language on the basis of a corpus, the linguist must also always bear in mind the way in which the texts in the corpus itself have been sampled from the overall population of texts. Analysing only a limited number of the concordance lines may be necessary from a practical point of view, but the analyst must bear in mind that the analysis is based on a sample of a sample.
It is also possible, at least in principle, to apply automatic procedures to thin concordance lines by selecting one or two examples which exemplify typical patterns of usage. This is an attempt to automate the work of finding typical patterns of usage in concordance lines, and may be useful for pedagogical, or for lexicographic applications.
Concordance output thinned in this way may be able to show something of the variety of different usages, but will not show patterns of repeated usage in and around the search term.
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Such a concordance must be read differently. The analyst should not look for repeated occurrences as evidence of typicality, because lines displaying some similarity will have been deleted, and a single typical example allowed to stand for them. Automatically thinning a concordance to produce typical examples, as discussed above, requires software to implement complex algorithms to interpret the patterns in the co-text and to select typical examples. It is sometimes useful for the analyst to be able to manually categorise the concordance lines, for example to classify different senses of a word which the analyst is able to assign by reading the concordance.
Categorising concordance lines can also be used as a way of manually thinning the concordance. Figure 9: Categorisation codes assigned to concordance lines using the Monoconc program. The categorisation has been done as follows: 'r' indicates 'fast' is an adverb, meaning quickly; 'j' indicates that 'fast' is an adjective, meaning quick; 'n' indicates that 'fast' is a noun, meaning to go without food, and 'i' indicates that 'fast' is part of an idiomatic expression, partially or fully de-lexicalised.
One line has also been tagged 'o', for 'other', and it is often useful to have such a category for problematic examples. Examining more concordances would probably should yield more evidence, making it possible to categorise this and other difficult examples, and would involve increasing the number of categories.
Stylistics and Elements of Style in Literature
This type of categorisation is therefore be seen as often a type of research where the concordance is a tool to help manual, qualitative linguistic analysis. Software which allows the annotation to be done on the electronic concordance data makes it possible to sort on the basis of the annotations, and to thin the concordance to leave only those lines with or without a certain manual categorisation.
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A human subject can then be shown the concordance lines with the node word invisible, and they must try to guess what the word is. An alternative, or additional, task is to ask the student to identify the wordclass.
Figure Concordance with node word concealed. It can be used as a language awareness training exercise for native or non-native speakers. Otherwise the user can save the concordance and then edit it in another application such as a text editor or word processor , or even simply print it out and black out the node word with ink. A related technique that could be useful for teaching purposes would be to conceal the significant collocates where they occur in the concordance lines.
Collocates are words which tend to occur frequently in the vicinity of the search term. Some concordance software applications can silently compute the significant collocates of the search term in the corpus, and represent these words in a particular way in the concordance view, for example by colouring them.
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In figure 11, the collocates are shown in bold and italic type to differentiate them from other words in the co-text. Figure collocates of the node word. This can help to identify patterns of co-occurrence in the concordance lines, particularly where there are too many examples to see in one screenful, or where the position of the collocate is variable. This is a useful function, because while the linguist may be able see repeated co-occurrences of words and structures, it is not possible to assess the statistical significance of these features simply by looking at them. This method of silently computing and displaying the collocates does risk obscuring the process of calculation from the user.
The linguist should remember that there are various ways to calculate collocates, and choices need to be made regarding, among other things, the collocation window, the basis for establishing what is the expected frequency of co-occurrence, the metric for assessing significance and the thresholds for frequencies and significance. Showing collocates in the concordance window should be seen as only a quick or preliminary indication of potential collocates, which are likely to require more focussed investigation and verification.