Distinctive lexical patternsin russian patient information leaflets: a corpus-driven study

Next, text-oriented n-grams include one condition n-gram (в том случае если`in the case when'), which is used to introduce certain condition related to administration of medicines, two transition n-grams (в связи с этим`in connection with this', не оказывает влияния на`it does not affect'), which, according to Hyland (2008: 14), help establish additive or contrastive links between information conveyed in PILs, and two text-deixis n-grams, e.g., см раздел о собые указания`see section special instruction', см раздел побочное действие`see section side effects', which help readers navigate through the contents or macro-structure of PILs.

Finally, stance/evaluation n-grams include one desire n-gram (может потребоваться коррекция дозы`dosage adjustment may be required'), which expresses a desirable course of action undertaken by patients in the event of any problems arising from the use of medicines, and four obligation/directive n-grams, starting with the third person present tense form of the verb следуетused in the impersonal form and followed by the infinitive `(one) should', predicative необходимо`(one) needs' followed by a single action verb in its infinitive form (необходимо/следует соблюдать осторожность при`(one) needs/should be careful when'), or centered around the verb in the infinitive form (направлять`send'), e.g., претензии потребителей направлять по адресу`customer complaints should be sent to'. All in all, this last group of n-grams is used to direct patients to carry out specific actions related to the use of medicines.

Conclusions

Inspired by theoretical insights from Russian, Polish and English phraseology, this methodologically-oriented study falls within the scope of frequency-driven distributional phraseology (Granger &Meunier 2008; Pqzik 2013), and its main goal was to identify the keywords and distinctive recurrent sequences of words in a sample of Russian patient information leaflets, and -- as a secondary goal -- to describe their discoursal functions. We also compared three methods of identifying keywords in texts (G2, Hedges' g and Neozeta) and further tested -- using Russian language material -- a recently proposed method of identification of recurrent multi-word units called Formulex (Forsyth 2015b).

The results revealed that that 22 keywords out of top-50 identified using three different metrics overlap with each other. Those keywords refer to administration of medicines to patients or to recommendations and advice offered to patients. As for the methods of keyword identification, the biggest overlap was recorded between G2 and Neozeta methods, while the application of Hedges' g yielded the most distinctive keywords. Finally, using Formulex method (Forsyth 2015b), we identified 50 n-grams with 4 or 5 words with the largest coverage in the Russian PILs. The qualitative analysis revealed that the largest group of those recurrent sequences of words perform referential functions, that is, they refer to various aspects of the use and administration of medicines.

In general, the findings revealed that the analyzed text type relies on a limited stock of single words and prefabricated chunks of text frequently used in Russian patient information leaflets, the items that account for the formulaicity of the text type under scrutiny. Also, the comparison of the performance of the three keyword metrics -- each based on different statistical assumptions -- enabled one to gain an insight into both similarities and differences ThisdistinctionisreferredtobyGabrielatos (2018: 252) askeyness-Svskeyness-D. between lexical patterns, which may come in useful for researchers using keywords in critical discourse analysis (CDA), among others.

As regards future avenues, more research is required to compare the performance of keyword metrics other than the ones used in this study. Also, to take into account distances between keyness scores, Gabrielatos (2018) proposes that candidate key words be clustered according to an effect size score, which is another idea for the future. As for comparisons of rankings of keywords, apart from Spearman Rank Correlation test used in this study, it is possible to use Mann Whitney U test on ranks to obtain another proximity score between different keyword metrics. Furthermore, it may be useful to consider employing other approaches to extract salient vocabulary from texts and study its aboutness, e.g., unsupervised topic modeling approaches (Silge& Robinson 2017) that help cluster individual words together providing an overview of the texts' semantic content SeeMurakamietal. (2017) forcomparisonsusingtopicmodellingtechniques, whereitisnotnecessarytouse a referencecorpus, with a traditionalkeywordanalysisinvolvingcomparisonsoffrequenciesofindividualitemsin a studycorpusand a referencecorpus.. Finally, more caution is required when it comes to selection of a reference corpus, which is one of the crucial decisions in the traditional keyword analysis. Although it is now known that there is no optimum size of the corpus (Gabrielatos 2018), it often happens -- usually in the case of smaller corpora with limited representativeness -- that many words do not occur in them. As a result, it is necessary to carefully think about an attenuating factor (i.e., its value) assigned to zero- frequencies in a reference corpus, and -- more broadly -- about the very characteristics of an appropriate reference purpose given the nature of the study corpus. Crucially, those decisions have important implications on the results of any keyword analysis. Last but not least, more comprehensive research is required to further compare different keyword metrics by conducting multiple studies on corpora of different size and make-up.

As for recurrent multi-word items, the Formulex method (Forsyth 2015b) should be compared with other metrics designed to extract structurally-complete or nonoverlapping sequences of words from texts, e.g., cascading serial algorithm (O'Donnell 2011), transitional probability metric (Appel&Trofimovich 2015), Independence- Formulaicity score (Pqzik 2015), frequency consolidation method (Buerki 2017), dependency-based approaches (Pqzik 2018). Furthermore, the results of a study like this one may be further verified using lemmatized Russian language data. It is also hoped that this study may be inspirational for future research on distinctive recurrent lexical patterns and on formulaicity of other genres of texts originally written in Russian. Finally, a comparison of the findings presented in this paper with the ones conducted for patient information leaflets written in other languages may yield comparable data that may be employed for developing domain-specific multilingual resources useful for translators, lexicographers or teachers of Russian for specific purposes (RSP/nOOHR), among others.

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