CC BY
13
UDC 81 322.2 UDC 373
DOI: 10.18413/2313-8912-2023-9-1-0-3
Sergei I. Monakhov1 Vladimir V. Turchanenko2 Dmitrii N. Cherdakov3
Terminology use in school textbooks: corpus analysis
1 Friedrich Schiller University Jena
1 Fuerstengraben, Jena, 07743, Germany
E-mail: sergomon@gmail.com
2 Institute of Russian Literature (Pushkinsky Dom) of the Russian Academy of Sciences
4 Makarov Emb., Saint Petersburg, 199034, Russia E-mail: vladimir. turchanenko@mail.ru
3 St Petersburg University 7-9 Universitetskaya Emb., Saint Petersburg, 199034, Russia E-mail: dm.cherdakov@,gmail.com
Received 23 January 2023; accepted 13 March 2023; published 30 March 2023
Acknowledgements. The reported study was funded by the Russian Foundation for Basic Research, Project number 19-29-14032 mk "Study of terminological subsystems of modern school textbooks in Russian with the help of word embedding models Word2Vec and neural networks".
Abstract. The article presents the methods and results of the study that investigated the use of terminology in textbooks for secondary schools in Russia. The data were taken from a full-text DIY corpus of 207 textbooks for grades 5-11. The toolkit included models trained with the Word2Vec algorithms driven by the ideas of distributional semantics. The models were used to improve traditional automatic term extraction based on word frequency statistics. Numerical representation of word collocation patterns and their semantic similarity enabled the following: more effective automatic term extraction with a clear dividing line between terminology per se and high-frequency common words; comparative analysis of inventory and functioning of terms in textbooks for different school subjects and grades; analysis of the dynamics of new terms entering educational and methodological complexes and insights into terminological relations between textbooks for different grades. The study included another DIY corpus compiled of scholarly articles across the subjects taught at school. It was used to identify differences in term use in textbooks and scholarly texts as well as in non-specific and popular science contexts. The latter was facilitated by the RusVectores word embedding model. The comprehensive analysis identified some patterns in term functioning relevant for particular school subjects or groups of subjects. The results were evaluated in view of the theory of text complexity, teaching methodology and didactics. The study found some contradictions between the expected and real text complexity. It also showed certain discrepancy between text complexity and basic didactic principles.
Keywords: Term; Terminology; School textbook; Text complexity; Word frequency; Vector representation; Word2Vec; Neural network
How to cite: Monakhov, S. I., Turchanenko, V. V. and Cherdakov, D. N. (2023). Terminology use in school textbooks: corpus analysis, Research Result. Theoretical and Applied Linguistics, 9 (1), 27-49. DOI: 10.18413/2313-8912-2023-9-1-0-3
УДК 81 322.2 УДК 373
DOI: 10.18413/2313-8912-2023-9-1-0-3
Монахов С. И.1 Турчаненко В. В.2 Чердаков Д. Н.3
Школьный учебный текст в аспекте терминоупотребления: корпусный анализ
1 Йенский университет им. Ф. Шиллера Фюрстенграбен, 1, Йена, 07743, Германия E-mail: sergomonagmail. com
2 Институт русской литературы (Пушкинский Дом) РАН наб. Макарова, 4, Санкт-Петербург, 199034, Россия E-mail: Vladimir. lurchanenkoamail. ru
3 Санкт-Петербургский государственный университет Университетская наб., 7-9, Санкт-Петербург, 199034, Россия E-mail: dm.cherdakova gmail.com
Статья поступила 23 января 2023 г.; принята 13 марта 2023 г.; опубликована 30 марта 2023 г.
Информация об источниках финансирования или грантах: Исследование выполнено при финансовой поддержке РФФИ в рамках научного проекта № 1929-14032 мк «Изучение терминологических подсистем современных школьных учебников на русском языке с помощью моделей анализа семантики естественных языков Word2Vec и нейронных сетей».
Аннотация. В статье излагаются методы и результаты анализа употребления терминологической лексики в современных школьных учебниках на русском языке. Основным материалом исследования является созданный исследовательский корпус, включающий тексты 207 учебников с 5-го по 11-й класс по 21 школьной дисциплине. Традиционный способ автоматического извлечения терминов, основанный на статистических показателях частотности словоупотребления, предлагается усовершенствовать с помощью создания моделей, обученных по алгоритмам Word2Vec, в основе которых лежат идеи дистрибутивной семантики. Применение этих алгоритмов, выражающее в числовом представлении сочетаемостное поведение слов и соответственно степень их семантической близости, позволило: в существенной мере устрожить результаты автоматического выделения терминов, отграничивая от них высокочастотные нетерминологические единицы; осуществить сопоставительную характеристику состава и употребления терминов в учебниках по разным предметам и разных ступеней обучения; проанализировать динамику пополнения терминологических систем внутри учебно-методических комплексов и охарактеризовать терминологические взаимосвязи между учебниками для отдельных классов. При помощи
специально созданного корпуса научных статей по тем дисциплинам, которые соответствуют предметам школьного обучения, были выявлены различия в употреблении терминов в школьной и научной сферах, а также (с использованием дистрибутивно-семантической модели, предоставляемой ресурсом RusVectores) в сфере общеупотребительной и научно-популярной речи. Для каждого из отмеченных аспектов анализа обнаружены значимые признаки в функционировании терминов, свойственные отдельным школьным дисциплинам или их группам. Полученные результаты оценивались в том числе в свете положений теории сложности текста и принципов дидактики и методики. Отмечены, в частности, случаи противоречия между показателями сложности текста и его предполагаемой трудности, а также неоднозначный характер взаимодействия меры сложности текста с ключевыми дидактическими началами.
Ключевые слова: Термин; Терминология; Школьный учебник; Сложность текста; Частотность слова; Векторное представление; Word2Vec; Нейронная сеть
Информация для цитирования: Монахов С. И., Турчаненко В. В., Чердаков Д. Н. Школьный учебный текст в аспекте терминоупотребления: корпусный анализ // Научный результат. Вопросы теоретической и прикладной лингвистики. 2023. Т. 9. № 1. C. 27-49. DOI: 10.18413/2313-8912-2023-9-1-0-3
1. Introduction
Russian terminology science is rich in history and scope, however, functioning of terms in school textbooks is still under-scrutinized. The V. A. Tatarinov
Comprehensive Encyclopedic Dictionary (Tatarinov, 2006) that embraces all possible advances in Soviet and Russian terminology science says nothing about the use of terminology in school-related textual contexts. At the same time, terms are used in school textbooks in thousands to reflect a system of scientific concepts. This makes the reported study relevant as a theoretical contribution to terminology science and research exploring didactic, social, and cultural issues. The study aimed to fill the gap in terminology science with the tools of corpus and computational linguistics that facilitate effective processing of big data.
Approaches to term extraction from large corpora vary (Korkontzelos, Ananiadou, 2014; Stepanova, 2017), however, top among them is the statistical approach that dates back to the 1960s (Piotrovskij, Yastrebova, 1969). This approach is based on the assumption that terms are considerably more frequent in specialized texts than in general texts. Thus,
the algorithm compares word frequency in the target corpus meant for term extraction with that in the reference corpus generally representing a collection of non-specialized text (Kilgarriff et al., 2014). As the results of traditional statistical approach to term extraction are still not satisfactory (Cabré et al., 2001), researchers are looking for options to improve its effectiveness with other methods (Mitrofanova and Zakharov, 2009; Lukashevich and Logachev, 2010; Nokel, 2012). We believe that a considerable improvement in automatic term extraction may be achieved through the use of the Word2Vec algorithms (continuous-bag-of-words (CBOW) and skip-gram). The algorithms follow the underlying idea of distributional semantics: the meaning of a word is derived from its lexical context, while mathematically it is represented as a sum of occurrences of the word in various contexts (Rohde et al., 2006; Jones, Mewhort, 2007; Durda and Buchananan, 2008; Turney and Pantel, 2010). Word embedding models trained with the Word2Vec algorithms use vector representations to measure the semantic similarity of words (Mikolov et al., 2013a; Mikolov et al., 2013b; Levy and
Goldberg, 2014; Brownlee, 2017). They have been gaining momentum in the recent decade; however, we have found no evidence of their application to explore the terminology of school textbooks. The major advantage of the proposed methodology is that it allows to track the behavior of semantically related groups of terms. This, in turn, opens up an opportunity to explore term functioning in view of its key property, i.e., its relation to a particular terminology system (Lejchik, 2007: 98-129).
The study outcomes, i.e., the stratification of terminology obtained from school textbooks with the help of corpus and computational linguistics, may also speak to the theory of text complexity. Complexity theory is witnessing an extensive use of automatic text processing and analysis tools; see reviews in (Solovyev et al., 2022; Solnyshkina et al., 2022) and examples of relevant studies in (Flor et al., 2013; Iomdin and Morozov, 2021; Glazkova et al., 2021; Sharoff, 2022). These methods are used to measure the complexity of various educational texts - a major focus of complexity studies. See, for example, a study investigating the complexity of textbooks with evidence taken from text corpora (Solovyev et al., 2018; Martynova et al., 2020).
Terminology is considered one of the lexical indicators of text complexity (Shpakovsky, 2007). By their nature, terms increase text complexity due to a number of factors. First, beyond the boundaries of specialized texts, terms are generally low-frequency words. It is commonly assumed that a high rate of low-frequency words increases text complexity. The assumption has been proved by recent cutting-edge research (Laposhina et al., 2022). Second, despite their reference to specific real objects, terms lean towards conceptual abstraction (Tatarinov, 2006: 231-234). This makes a text more abstract and, hence, more complex (Schwanenflugel, 1991; Fisher et al., 2016). Third, terms, as a rule, have a complex semantics which is unlikely to be familiar to
laymen (Mikk, 1981: 65). Semantic complexity is difficult to measure formally (Morozov and Iomdin, 2019). However, it should be borne in mind that terms, as part of a textbook, are the words that are supposed to become known. This is the reason why it is fair to describe them as "unknown".
When it comes to textbooks or educational and methodological complexes, it is important to keep in mind the dichotomy found in complexity studies: absolute vs. relative text complexity. The former is a total of its objective features, while the latter depends on external factors, namely, cognitive abilities of the reader (Solnyshkina and Kiselnikov, 2015: 86-87; Solnyshkina et al., 2022: 20). High frequency of terms increases text complexity, however, their regular occurrence in a textbook reduces it (Mikk, 1981: 67). The same passage containing terms that are supposed to be mastered is likely to be perceived as more or less difficult depending on its position at the beginning or at the end of the text, respectively. This is due to the knowledge and certain familiarity with terminology that students accumulate over the course of study.
As regards didactics, textbook complexity and difficulty are subject to the dichotomy of didactic principles, i.e., scientificity and comprehensibility. A textbook should be in line with state of the art in science and research. This, inter alia, includes terminology which makes an educational text inevitably complex. While being complex, a textbook should be comprehendible. A failure to observe these principles affects educational effectiveness of a textbook.
2. Materials and methods
2.1. Development of target corpora
The reported study included several stages. Among them is the development of research/target corpora, their vectorization, and data clustering.
The priority task was to develop a target corpora of school textbooks. The corpus included 207 textbooks for grades 5-11 published by Prosveshcheniye Publishers. The
research team obtained a permission from the publisher to use their texts for research purposes. The corpus was compiled in 2020. As of 2020, all the indexed textbooks were approved for the use at schools by the Ministry of Education of Russia. The corpus was developed in several stages: scanning, OCR, processing to delete non-letter and punctuation symbols and harmonize the letter case. Special software was used for POS tagging and lemmatization. The corpus totaled a little more than 13 965 000 words. It was then split into subcorpora matching school subjects (a total of 21 subjects): Algebra (18 textbooks; 1 144 089 words), Astronomy (2; 89 574), Biology (21; 1 125 648), General History and History of Russia (15; 973 498), Geography (8; 512 173), Geometry (8; 370 054), Natural Science (2; 158 665), Visual Arts (8; 283 608), Computer Science (6; 284 683), Literature (18; 3 939 054), Mathematics (10; 525 035), Mathematical Analysis (14; 1 134 786), History of World Art (2; 33 130), Music (4; 76 241), Social Science (12; 505 822), Law (2; 171 349), Russian Language (18; 1 131 575), Arts and Crafts (4; 163 574), Physics (15; 1 098 625), Physical Education (7; 301 371), Chemistry (13; 543 283). Then, every subject-specific subcorpus was further split by school grade.
As textbook terminology was investigated comparatively, the reported study needed another research corpus (see Section 3.2 below). This corpus included relevant scholarly articles selected according to a set of principles. We chose articles published in the period from 2016 to 2021 in high-citation wide-scope journals. Each subject area was covered with 2-5 journals. The share of articles from each of the journals was determined by the journal citation index. The corpus included titles, abstracts, and the main body of articles and reports published in key journal sections. For example, out of 100 plus journals in geography indexed in the Russian Index of Science Citation, we only selected three wide-scope journals with strong citation rates: Geography and Natural Resources; Moscow University Bulletin. Series 5,
Geography; Proceedings of the Russian Academy of Sciences, Geography Series. The journals have similar average citation rate of 8.51, 9.72, and 9.00, respectively. For this reason, their share in the corpus was almost equal and accounted for 50, 46 and 40 articles, respectively (136 articles in total). The corpus of scholarly articles was split into subcorpora. On the whole, they match the subcorpora of school textbooks. The exceptions are few: the corpora of scholarly articles did not include the textbook subcorpora in Natural Science and Arts and Crafts; the school subcorpora in Mathematics, Algebra, Geometry, and Mathematical Analysis corresponded to a single subcorpus of scholarly articles called Mathematics. The same is true for the Visual Arts and History of World Art textbook subcorpora that corresponded to the Art scholarly subcorpus. The processing of texts for the corpus of scholarly articles followed the same stages as that for the textbook corpus. The size of each subcorpus of scholarly articles was no less than 75% the size of the corresponding thematic subcorpus of school textbooks. E.g., the size of the Geography subcorpus was, respectively, 434 000 words and about 512 000 words for the scholarly and textbook corpus; Biology 853 000 words and about 1 126 000 words; History about 902 000 words and about 973 000 words. The corpus of scholarly articles totaled about 10 795 500 words.
Once the corpora were ready, they were uploaded on the Sketch Engine at https://www.sketchengine.eu. The platform was used for automatic extraction of term candidates based on the comparative analysis of word frequency in target and reference corpora (see above for details). The reference corpus was the Russian Web 2011 Sample (ruTenTen11), available in the Sketch Engine and containing over 900 million word uses from Russian-language Internet texts.
2.2. Automatic term extraction and consequent data vectorization
One-word and multi-word term candidates followed different extraction
algorithms. The keyness score was calculated for every one-word lexical unit with the minimum frequency threshold of three according to the formula ((Lt * 1,000,000 / Ct) +1) / ((Lr * 1,000,000 / Cr) +1), where Lt is the word frequency in the target corpus, Ct the total number of tokens in the target corpus, Lr the word frequency in the reference corpus, Cr the total number of tokens in the reference corpus. A one-word lexical unit became a term candidate if the keyness score was higher than 1. Let us compare, as an example, the keyness score for the words from different subcorpora of the textbook corpus. The Algebra subcorpus (grades 7-9): многочлен (polynomial) - 743.2, множитель (multiplier) - 380.4, парабола (parabola) - 322.3; the Russian Language subcorpus (grade 5): существительное (noun) - 479.4, падеж (case) - 231.4, антоним (antonym) - 170.4; the Biology subcorpus (grade 9): фотосинтез (photosynthesis) - 562.3, фенотип (phenotype) - 66.1, цитоплазма (cytoplasm) - 11.2; the Chemistry subcorpus in the corpus of scholarly articles: макромолекула (macromolecule) - 306.5, адсорбция (adsorption) - 103.042, полимеризация (polymerization) - 67.9; the Astronomy subcorpus: полуось (semiaxis) - 197.4, галактика (galaxy) - 94.6, цефеиды (Cepheids) - 31.3.
The extraction of multi-word term candidates was in two stages. First, we identified word combinations with the minimum frequency threshold of three that had positive Log-Dice scores. This was calculated according to the formula 14 + log(2(|XHY|) / (|X| + |Y|)), where |X| is the absolute frequency of the first item in the word combination in the subcorpus, |Y| the absolute frequency of the second item in the word combination in the subcorpus, and |XRY| the absolute frequency of the word combination in the subcorpus. Once the term candidates were selected, their keyness score was calculated according to the above formula. Let us compare, as an example, the keyness score for the collocations from
different subcorpora of the textbook corpus. The Algebra Subcorpus (grades 7-9): график функции (function graph) - 725.9, натуральное число (natural number) - 200.9, линейная функция (linear function) - 97.6; the Russian language subcorpus (grade 5): часть речи (part of speech) - 428.2, единственное число (singular number) -222.4, прошедшее время (past tense) - 76.1; the Biology subcorpus (grade 9): бесполое размножение (asexual reproduction) - 240.4, пищевая цепь (food chain) - 190.9, генная инженерия (genetic engineering) - 67.0; the Chemistry subcorpus of the corpus of scholarly articles: элементный анализ (elemental analysis) - 145.0, реакционная масса (reacting mass) - 75.4, буферный раствор (buffer solution) - 65.7; the Astronomy subcorpus: красное смещение (red shift) - 120.4, дыра Локмана (Lockman Hole) -81.0, солнечный ветер (solar wind) -51.2.
The obtained lists of one-word and multi-word terms were sorted in discerning order from the highest to the lowest keyness score. Further processing was made for the first 1 000 terms from the textbook corpus and the first 2 000 terms from the corpus of scholarly articles.
One of the key challenges of the proposed framework is the identification in the obtained word lists of terms per se and non-terms that show term-like behavior in the textbook corpus, i.e., have high frequency in the target corpus and low frequency in the reference corpus. Here and in what follows such units will be referred to as pseudoterms. This designation is conventional since automatic delineation between terms and pseudoterms may end up labelling some of the terms per se as pseudoterms.
To optimize the obtained results, we used the Word2Vec algorithms. They facilitated vectorization of target corpora as well as development and training of word embedding models. The models, in turn, were used to identify the degree of syntagmatic similarity among automatically extracted terms in each of the DIY subcorpora. Each
subcorpus had two models. One was used for one-word terms, the other for multi-word terms (bigrams and trigrams). The training of models involved the following stages: 1) frequency analysis for each word in the corpus; 2) frequency-based sorting, rare words deleted; 3) Huffman binary tree coding to reduce computational complexity of the algorithm; 4) vectorization of every single word of the corpus. Vectors show the number of cases a given word occurs in the same context window with other high-frequency words from a given corpus. The context window indicates the maximum range between the target and the predicted word in a sentence; 5) using the obtained vectors as an input for a feedforward neural network. The neural network was trained to predict the context by a target word or predict a word by a target context.
Vector representation of words is a tool to evaluate semantic similarity of any pair of words through calculating the cosine measure between their vectors. We calculated the cosine similarity CS = u * v / (||u|| * ||v||) for each word pair. CS was within the range [0,1], where 1 denotes the identity of vectors, i.e., identical contexts of target words implying their semantic similarity; and 0 denotes vector orthogonality, implying a lack of common contexts and, hence, common semes. Compare, as an example, cosine similarity of two pairs of words in the Russian Language subcorpus textbook: суффикс (suffix) and окончание (ending) - 0.80; суффикс (suffix) and груша (pear) - 0.18.
The results of vectorization were used to enhance the effectiveness of automatic term extraction. This was done in two ways depending on the target corpus (textbooks or scholarly articles). The reason behind this differentiation is the different nature of texts in each of the corpora - the corpus of scholarly articles is more consistent lexically and structurally.
Semantic mapping was chosen for terms extracted from the textbook corpus. The subcorpus-related maps of term candidate distribution in trained word embedding
models were built using t-distributed stochastic neighbor embedding (t-SNE). For visualization, the maps were projected onto a two-dimensional plane from high-dimensional vector space. See examples in Figures 1-2.
We assumed a high probability of black spots on the map denoting clusters with small cosine distance of terms per se. Pseudoterms, on the opposite, were expected to be scattered across the rest of the map. K-means were calculated to cluster the points in the plane by their coordinates. Each of the obtained clusters was labeled as either containing terms or pseudoterms. Each semantic map had about 20 clusters and all available points were distributed across them. The following factors were accounted for in cluster labelling: 1) specific share of words that occur within the cluster as independent words or as part of bigrams or trigrams. Assumingly, terminology-rich clusters have a higher word recurrence; 2) specific share of multi-word lexical units within a cluster. It is assumed that terminology-rich clusters have more multi-word units as automatic term extraction is more effective with multi-word candidates; 3) specific share of term candidates within the cluster that match the terminology used in the Federal State Educational Standards of Russia. It is assumed that terminology-rich clusters have more such matches. Taking into account the outlined factors, we calculated a single metric varying from 1 to 7 200 for each cluster with a high probability of a cluster to contain terms or pseudoterms, respectively. As an example, compare the obtained lists of terms for different subcorpora. The Russian Language subcorpus (grade 9) with the metric equaling 1: русскийязык (Russian language), синтаксис (syntax), фонетика (phonetics), орфография (orthography), историяязык (history_language), слово (word), неологизм (neologism), морфема (morpheme), этимология (etymology), старославянскийязык (Old Slavic_lan-guage), значение (meaning),
древнерусскийязык (Old Russian_langua-ge), современный_русский_язык
(modern_Russian_language), славя-
нский язык (Slavic_language).
Figure 1. Semantic map representing the distribution of term candidates. The Algebra Subcorpus (grade 9)
Рисунок 1. Семантическая карта распределения кандидатов в термины. Подкорпус «Алгебра» (9 класс)
Figure 2. Semantic map representing the distribution of term candidates. The Russian Language Subcorpus (grade 6)
Рисунок 2. Семантическая карта распределения кандидатов в термины. Подкорпус «Русский язык» (6 класс)
For the same subcorpus, the metric with the value 3 600 returned the following words: ветер (wind), дерево (tree), осина (aspen), оса (wasp), колокольчик (bell), ель (spruce), соловей (nightingale), ямщик (coachman), рябина (ashberry), пирог (pie), туман (fog), гром (thunder), туча (thundercloud), роса (dew). Another example is the Geography subcorpus (grade 7) with the metric equaling 4.6. The list of terms included воздушныймасса (air_mass),
форма_рельеф (shape_relief), котловина (basin), высотныйпоясность
(altitude_zonality), высотныйпояс
(altitude_zone), землетрясение (earthquake), кристаллический_фундамент (crystalline _foundation), платформа (platform), муссон (monsoon). For the same subcorpus with the metric value 16.8 the word list contained причинаобразование (reason_formation), французскийязык (French_language), картаприложение (map_application), сочетание_фактор (combination_factor), картаевразия (map_Eurasia), деление земля (demarcation_Earth), бразильс-кийкарнавал (Brazilian_carnival),
благосостояние_население (wellbeing_po-pulation), главныйзанятие (major_activity).
To enhance the effectiveness of automatic term extraction from the corpus of scholarly articles, another approach was used. The automatically extracted term candidates were sorted by their semantic distance from the hypothetical center of the lexical system used for general communication purposes. The center was the calculated average value O of all vector representations of the word embedding model that was trained with the Russian National Corpus and is now available on the RusVectores platform (Kutuzov, Kuzmenko, 2017). (1) Vector representation of each term candidate Ci in the original list {LCj}, so that Ci G {LCj}, was compared with О through calculating the cosine distance between the vectors 6(Ci) = cos(Ci, О); (2) the candidate with the cosine distance 0 was assigned index 1 indicating a high term probability. It was subsequently deleted from the list so that Ci => K1 and K1 G {KC} €
{LCj+1}, if 0(Ci) = argmax(0i ... 0n); (3) steps 1 and 2 repeated for Ci+1 G {LCj+1} until the list was empty so that {LCn} = 0 and {KC} = {LCj}. Ultimately, from among the hierarchy of indexes {i ... n} in the list {KC}, the index k (i < k < n) was chosen so that the subset of candidates {Kk ... Kn} could be excluded from the list {KC} as containing the least probable terms. At this stage of the study, the cut-off point for each subject area was determined by expert decision. Compare, as an example, the first and the last 15 term candidates from the list of the Russian Language subcorpus compiled following the outlined approach. The first 15 candidates are экспликация (explication), предикативность (predicativity), именование (nomination), модус (mode), денотат (denotatum), интенция (intention), лексема (lexeme), актант (actant), пресуппозиция (presupposition), дескрипция (description), модальность (modality), семантика (semantics), референция (reference), предикат (predicate), пропозиция (proposition). Among the last 15 candidates from the list are мальчик (boy), варвара (varvara), петя (petya), наци (nazi), бенефициант (beneficiary), господин (gentleman), скотина (cattle), парная (steam room), зеница (pupil), макар (makar), жучок (bug), обида (offence), скука (boredoom), тополь (poplar), червяк (worm).
Once the outlined methodology was applied to the original list of term candidates, the total number of one-word and multi-word term candidates for the textbook corpus accounted for 26 328 with the following subcorpora distribution: Algebra - 1 526, Astronomy - 456, Biology - 2 324, General History and History of Russia - 2 491, Geography - 1 635, Geometry - 570, Natural Science - 198, Visual Arts - 808, Computer Science - 682, Literature - 2 306, Mathematics - 903, Mathematical Analysis -635, History of World Culture - 215, Music -46, Social Science - 2 286, Law - 404, Russian Language - 2 633, Arts and Crafts -406, Physics - 2 836, Physical Education -1 161, Chemistry - 1 807. The same indicator
for the corpus of scholarly articles was 15 247 with the following subcorpora distribution: Astronomy - 1 060, Biology - 1 157, Geography - 1 112, Computer Science - 896, Art - 1 182, History - 891, Literature Studies - 1 101, Mathematics - 753, Musicology -955, Social Science - 1 116, Law - 1 169, Russian Language / Linguistics - 945, Physics - 999, Physical Education - 892, Chemistry - 1 019.
3. Results and discussion
3.1. Terms per se and high-frequency non-terms
Data vectorization increases the effectiveness of automatic term extraction. It also creates a foundation for further analysis of term functioning in school textbooks and beyond.
Notably, automatic term extraction from the textbook corpus generated a considerable number of pseudoterms, i.e., lexical units with high relative frequency in the target corpus that were discarded after vectorization. Pseudoterms show different behavior in subject-specific subcorpora both by quantity and thematically. Used in school textbooks in different subjects, pseudoterms comprise part of special lexical and semantic groups. These lexical groups may be of interest to scholars focusing on teaching and learning methodology for schools and other educational settings.
For obvious reasons, a range of textbooks in particular subjects have pseudoterms that describe subject-specific real-life phenomena. Below is an excerpt from an extensive list of high-frequency plant names from the Biology subcorpus: абрикос (apricot), акация (acacia), арахис (peanut), астра (aster), бегония (begonia), белена (henbane), бузина (elderberry), вишня (cherry), георгин (dahlia), горох (pea), дуб (oak), дурман (thorn apple), ель (spruce), земляника (wild strawberry), ива (willow), капуста (cabbage), картофель (potato), кипарис (cypress), кислица (oxalis), клевер (clover), кукуруза (corn), ландыш (lily-of-the-valley), лещина (hazelnut), липа (linden), лиственница (larch), люпин (lupine),
люцерна (medick), малина (raspberry), можжевельник (juniper), нарцисс (daffodil), одуванчик (dandelion), ольха (alder), орешник (hazel tree), орхидея (orchid), осина (aspen), осока (sedge), пальма (palm), папоротник (fern), пеларгония
(pelargonium), пихта (fir tree), подорожник (plantago), подсолнечник (sunflower), пшеница (wheat), пырей (elytrigia), редис (radish), редька (winter radish), репа (turnip), рыжик (orange milk cap), рябина (ashberry), саксаул (saxaul), сирень (lilac), слива (plum), сосна (pine), томат (tomato), тополь (poplar), тюльпан (tulip), фасоль (bean), фиалка (violet), фикус (ficus), хлопчатник (cotton plant), хризантема (chrysanthemum), цикорий (chicory), шиповник (rose hip), эвкалипт (eucalyptus), яблоня (apple tree), ясень (ash), ячмень (barley).
Another reason for groups of pseudoterms to appear in subject-specific subcorpora is a methodological tradition. The Literature subcorpus (grades 10-11) is marked by a group of automatically extracted abstract nouns with suffixes common for high-flown or purely bookish vocabulary: безверие (faithlessness), возмездие (retribution), высокийпредназначение (lofty_mission), дарование (talent), духовныйвозрождение (spiritual_revival), жертвенность
(beneficence), искание (pursuit), искренность (sincerity), личный_достоинство (personal_dignity), мечтание (dreaming), мироздание (universe), мироощущение (philosophy of life), миросозерцание (worldview), нравственн ыйчувство
(moral_feeling), обличение (denunciation), поэтическийвдохновение (poetic_inspi-ration), предчувствие (presentiment), преображение (transfiguration), прозрение (insight), простодушие (guilelessness), раздумье (reflection), самопожертвование (self-sacrifice), скитание (wandering), сострадание (compassion), сочувствие (empathy), страдание (suffering), счастие (happiness), тщеславие (vanity), человечность (humanity), чужбина (foreign lands). These words are selected as term candidates due to their high frequency in
literature textbooks, where they are used to discuss the artistic meaning of literary works or certain aspects of writers' biographies.
The Russian Language subcorpus (grades 5-9) is even more remarkable when it comes to pseudoterms. They include numerous words and word combinations that describe landscapes or natural phenomena. Among them such words and phrases as аист (stork), былинка (blade of grass), верба (American willow), ветер (wind), воробей (sparrow), восходсолнце (sunrise_sun), вьюга (blizzard), глубокийозеро (deep_lake), голубойнебо (blue_sky), гроза (thunderstorm), гром (thunder), дождик (drizzle), долгийзима (long_winter), дубрава (oak forest), дымок (smoke), ель (spruce), жаворонок (lark), журавль (crane), заяц (hare), зимний_утро
(winter_morning), зяблик (chaffinch), ива
(weeping willow), изморозь (rime ice), иней (hoarfrost), камыш (reed), крапива (nettle), кукушка (cuckoo), лазурь (azure), ландыш (lily-of-the-valley), лесной_озеро
(forest_lake), лесной_поляна (forest_glade), лесок (small forest), липа (linden), лиса (fox), листопад (leaf fall), метель (blizzard), наст (snow crust), начало осень (beginning_fall), облачко (cloud), овраг (ravine), озимь (winter sowing), орешник (hazel tree), оса (wasp), осина (aspen), осока (sedge), перелесок (shaw), песок (sand), пичужка (bird), подосиновик ^spen bolete), подснежник (snowdrop), поздний_осень (late_autumn), пороша (dusting of snow), пригорок (hillock), проталина (thaw patch), ракита (riverside willow), родной_природа (Russian_nature), роса (dew), роща (grove), рябина (ashberry), свежий ветер (breeze), синий небо (blue_sky), синица (great tit), сирень (lilac), скворец (starling), снегирь (bullfinch), снежный_буря (snow_storm), сова (owl), соловей (nightingale), старый-_дуб (old_oak), стужа (cold), сумрак (dusk), туман (fog), туча (thunder cloud), фиалка (violet), холодныйветер (cold_wind), чаща (thicket), чибис (peewit), шалаш (hut), шмель (bumblebee). It was
found that primary school textbooks are dominated by vocabulary describing nature and natural phenomena (Laposhina et al., 2019). It should be noted, however, that the Russian Language textbooks are supposed to contain thematically balanced, emotionally relevant and stylistically diverse vocabulary to effectively fulfill their educational potential. In this regard and despite the methodological tradition, it is fair to suppose that intuitive and now mathematically proven thematic consistency of vocabulary found in textbooks in the subject Russian language does not promise much effectiveness in terms of educational outcomes.
It is of interest to compare the quantity of terms per se and pseudoterms in the total high-frequency vocabulary found in different subject-specific subcorpora. Here, textbooks in exact and natural sciences form a juxtaposition with those in arts and humanities with terms per se prevailing in the former and pseudoterms more common for the latter (See Figure 3). Interestingly, the Russian Language subcorpus is unexpectedly term-rich with almost as many terms as in the subcorpora comprising textbooks in exact and natural sciences. This is indicative of extensive terminology contained in the Russian Language textbooks. Along with this, the Literature subcorpus in Figure 3 is shown to have fewer terms than the Russian Language textbooks despite the obvious focus of both disciplines on linguistic issues.
The reasons explaining the breakdown in Figure 3 are not trivial. The selection of terms from among high-frequency candidates was facilitated by special algorithms that took into account (a) regular occurrence of terms in the text, which (b) showed syntagmatic patterns similar to those of the terms from the same subject area. A failure to observe the requirement (a) may lead to a failure in the identification of a term from among high-frequency candidates in the target corpus, while a failure to observe (b) may result in misidentification of a term as a pseudoterm.
Figure 3. Share of terms in the total number of high-frequency words in the textbook subcorpora Рисунок 3. Доля терминов от общего числа высокочастотной лексики в подкорпусах корпуса школьных учебников
With the above said, it may seem paradoxical that the Natural Science subcorpus ranks so low as regards the number of terms per se. The Natural Science subcorpus (Nat. Sci.) includes basic upper secondary school textbooks in physics, astronomy, chemistry, and biology. A low share of terms in the subcorpus is not only due to the overall low number of terms, but also due to the specifics of their functioning. These textbooks provide a very brief overview of relevant subject areas which explains low term frequency and lack of patterns in the contextual behavior of groups of terms.
Another salient example is the Music subcorpus. Semantic mapping showed a very low number of terms in comparison with other textbook subcorpora. This indicated that groups of terms extracted automatically with the keyness score (in particular, names of music genres, e.g., cantata, symphony, suite,
etc.) do not show similar contextual behavior and are not counted as terms.
Similar cases may be observed in some parts of a subject-specific subcorpus that includes vocabulary of a particular educational and methodological complex. Let us consider as an example the Russian Language educational and methodological complex (grades 5-11) edited by L. Verbitskaya1. The textbook for grade 5 features the word combination орфографическийправило (spelling_rule) which is not identified as a term in this particular textbook. However, it is identified as such in textbooks for grades 6-7 where it is frequently used and, importantly, belongs to the term cluster that includes such words as орфограмма (orthogram),
1 Russian Language: textbooks for educational organizations / Under the general editorship of Academician of the Russian Academy of Education L. Verbitskaya, Prosveshchenie, Moscow, Saint Petersburg, 20182019.
правописаниегласный (spelling_vowel), правописаниеприставка (spelling_prefix), правописаниеслово (spelling_word),
правописаниесуффикс (spelling_suffix), ударение (stress), условиевыборбуква (condition_choice_letter), etc.
Needless to say, a high share of terms among high-frequency textbook vocabulary is indicative of considerable text complexity. On the other hand, absolute complexity of an educational text resulting from abundant terminology is somewhat compensated by a regular and systemic use of groups of terms that form a semantic whole. By contrast, the presence of terms with insufficient frequency and/or contextual similarity with the words close in meaning may impede understanding of the text while simultaneously reducing its absolute complexity.
3.2. Terminological links
In view of the above, of special importance are the terminological links established both between different parts of one and the same textbook and different textbooks within one and the same educational and methodological complex. These links implement prospection and retrospection fundamental to any text and correspond to the didactic principles of "advance training" and "revision and consolidation", respectively. At the outset, it is interesting to assess the dynamics of term accumulation and specific contribution that textbooks for different grades make into the development of subject-specific terminology systems. The following algorithm was developed to solve this task: 1) every educational and methodological complex was broken down into passages matching the length of one thematic section (1 000 words on average); 2) the number of terms and term combinations previously assigned to terminology systems of different grades were calculated for each passage; 3) the total number of new terms that a particular
textbook contributes to the general terminology system of the educational and methodological complex was calculated; 4) the number of terms from the terminology systems of all grades was calculated for each passage.
Below is an example that shows the dynamics of new terms entering the terminology system of the Russian Language educational and methodological complex edited by L. Verbitskaya. Figure 4 depicts the share of new terms for every grade, while Figure 5 shows the same dynamics plus the distribution of terms in different parts of textbooks for different grades (textbooks for grades 10-11 are shown together with the black line).
As is seen, the textbook for grade 5 is very much the leader by the number of terms found in each of the four passages of the educational and methodological complex. Put otherwise, the knowledge of subject-specific terminology is developed at an early stage of education, while every new stage facilitates its revision. Upper secondary school textbooks almost never introduce new terms as they are primarily meant for consolidation and revision.
The study compared the above dynamics with that in the educational and methodological complex in Literature developed by V. Korovina et al. (grades 5-9) and V. Korovin et al. (grades 10-11)2. The terminology system of upper secondary school textbooks was found to be very much updated as compared to the choice of terms for earlier grades (see Figure 6). The number of new terms entering the Russian Language and Literature upper secondary textbooks is different as these textbooks are meant for different levels of study: basic vs. advanced, respectively.
2 Literature: textbooks for educational organizations / Edited by V. Korovina, Prosveshchenie, Moscow, 2012-2013 [Grades 5-9]; Literature: textbooks for educational organizations / Edited by V. Korovin, Prosveshchenie, Moscow, 2012-2019 [Grades 10-11].
Figure 4. The dynamics of new terms entering the terminology system of the Russian Language educational and methodological complex edited by L. Verbitskaya
Рисунок 4. Динамика пополнения терминосостава в учебно-методическом комплексе «Русский язык» под ред. Л. А. Вербицкой
Figure 5. The dynamics of new terms entering the terminology system of the Russian Language educational and methodological complex edited by L. Verbitskaya with the distribution of terms in different parts of textbooks for different grades
Рисунок 5. Динамика пополнения терминосостава в учебно-методическом комплексе «Русский язык» под ред. Л. А. Вербицкой с указанием на терминоупотребление в разных частях учебника
О 10 20 30 40
Textbook parts
Figure 6. The dynamics of new terms entering the terminology system of the educational and methodological complex in Literature edited by V. Korovina et al. (grades 5-9) and V. Korovin et al. (grades 10-11) with the distribution of terms in different parts of textbooks for different grades Рисунок 6. Динамика пополнения терминосостава в учебно-методическом комплексе по литературе В. Я. Коровиной и др. (5-9 классы), В. И. Коровина и др. (10-11 классы) с указанием на терминоупотребление в разных частях учебника
Grade-wise clustering of terms makes it possible to analyze how close are the terminological links between specific textbooks within one educational and methodological complex. The analysis algorithm is described below:
1) a set of terminology clusters {T5, T6, T7, T8, T9, T10-11} that include term t of a particular subject of a particular grade was
identified;
2) an overlap measure Ti П Tj, i Ф j was calculated for the terms in each pair of the identified terminology clusters;
3) the identified measures were compiled into a matrix. See an example below with term морфема (morpheme) from the educational and methodological complex Russian Language edited by L. Verbitskaya.
Table 1. General terms share in clusters with the term морфема (morpheme) in the educational and methodological complex Russian Language edited by L. Verbitskaya
Таблица 1. Доля общих терминов в кластерах, содержащих термин «морфема», в учебно-методическом комплексе по русскому языку под ред. Л. А. Вербицкой
Grades 5 6 7 8 9 10
5 1.00 0.74 0.76 0.76 0.74 0.74
6 0.56 1.00 0.62 0.55 0.61 0.70
7 0.64 0.69 1.00 0.75 0.75 0.71
Grades 5 6 7 8 9 10
8 0.58 0.55 0.67 1.00 0.61 0.59
9 0.70 0.75 0.83 0.75 1.00 0.75
10 0.60 0.74 0.68 0.63 0.65 1.00
The matrix is read row-wise. The figures in cells given within the range [0,1] indicate the share of terms in a cluster that include term t from a textbook Ti and overlap with the terms in a cluster that include term t from a textbook Tj;
4) the matrix was analyzed for the maximum value, i.e., two clusters with the closest terminological links from different textbooks. In the above table this value is 0.83. This is the share of terms in the cluster that includes the term морфема (morpheme) in the textbook for grade 9 that overlap with the terms in the cluster that includes the term морфема (morpheme) in the textbook for grade 7;
5) pairs of textbooks Ti and Tj with the greatest overlap were logged in the forward_classes glossary if i < j, or in backward_classes glossary if i > j. Thus, forward_classes contained cases of cataphoric repetitions. This means that a terminology system of an earlier grade is part of an extensive terminology system of more advanced stages of training. In its turn, backward_classes contained anaphoric repetitions. They happen when a terminology system of an advanced stage of education repeats the terminology system of an earlier grade;
6) the data obtained for specific terms found in the educational and methodological complex were aggregated by pairs of grades.
An example below illustrates the hierarchy of terminological similarity in the Russian Language educational and methodological complex edited by L. Verbitskaya. The hierarchy is grade-wise. Grades are given in round brackets before the
colon. Grades 10 and 11 are designated as 10. The figure after the colon indicates the number of unique terms with the maximum overlap of clusters that contain these unique terms in a given pair of textbooks. The indicators are sorted in descending order.
1) forward_classes - {(5, 6): 48, (7, 8): 46, (7, 10): 35, (5, 10): 26, (6, 10): 22, (7, 9): 21, (5, 8): 19, (8, 9): 19, (9, 10): 13, (5, 9): 9, (8, 10): 8, (5, 7): 7, (6, 9): 6, (6, 8): 4, (6, 7): 1},
2) backward_classes - {(10, 6): 62, (10, 9): 51, (9, 7): 44, (8, 6): 44, (7, 6): 36, (10, 8): 17, (10, 7): 10, (6, 5): 10, (7, 5): 8, (9, 8): 6, (8, 7): 6, (10, 5): 5, (9, 6): 5, (8, 5): 3, (9, 5): 1}.
It should be concluded that the educational and methodological complex in question has the closest thematic links in textbooks for grades 5 and 6 and grades 7 and 8. The textbooks for grades 6 and 7 are the least close thematically. As regards revision and consolidation of new knowledge, the closest thematic links are shown by the textbooks for grades 10 and 6, as well as grades 10 and 9. The least close links are observed between the textbooks for grades 9 and 5.
3.3. Using terminology in different domains
The multifaceted comparative analysis is yet another research opportunity that emerged as an outcome of target corpora vectorization (the textbook corpus and corpus of scholarly articles). Below is the discussion of just one aspect of a possible comparative study. This section compares the functioning of terms in educational and scholarly texts
with their use in non-specific and popular science contexts1.
The comparative analysis was facilitated by the RusVectores model ruwikiruscorpora-
superbigrams_skipgram_300_2_2018. The model was trained with 600 mln. words of the Russian National Corpus and Wikipedia articles in December 2017 (hereinafter the RusVectores model). It is the only model with all possible productive bigrams glued together, regardless of their frequency. The ability of the model to recognize bigrams is a must-have since the terms under study include both one-word and multi-word lexical units.
Next steps included:
1) development and training of the new word embedding model Word2Vec (hereinafter the Corpus model). The Corpus model was trained with the data from the single corpus of school textbooks and scholarly articles. The vector embedding size was 300. This adjustment was necessary as the original model had a size of 32 incompatible with the RusVec-tores model;
2) selection of vector representations of all the terms from the single corpus that were found in the RusVectores model glossary;
3) distribution of the selected vector representations across the four groups: dis-tance_textbooks_wiki (vector representations of school textbooks terms in the RusVectores model), distance_textbooks (vector representations of school textbooks terms in the Corpus model), distance_articles_wiki (vector representations of terms from scholarly articles in the RusVectores model), dis-tance_articles (vector representations of terms from scholarly articles in the Corpus model);
3 Another important aspect here is the comparison of semantic maps of term use in school textbooks and
scholarly articles. This aspect of comparative analysis
reveals pronounced differences in term functioning in
4) obtention of all possible pairwise combinations of terms assigned to one of the four groups of vector representations and calculation of cosine similarity CS = u * v / (||u|| * ||v||) for each pair, so that CS is within the range [0,1], where 1 denotes vector identity, and 0, vector orthogonality;
5) processing of all the data obtained for the four groups with one-way analysis of variance (ANOVA) to determine the ratio of systematic (intergroup) variance to random (intragroup) variance. The CSMi measure with i = 1,2,3,4 calculated for each of the four groups, is the arithmetic mean of all pairwise measures of cosine similarity. It gives a general understanding of how closely related the terms from a particular group are in vector space, i.e., how semantically cohesive the group of terms is;
6) analysis of statistically significant differences between the four groups of cosine similarity measures for all the domains under study. Since the analysis of variance does not show which particular groups differ from each other, it was followed by a posteriori comparisons, i.e., pairwise comparisons of the four groups with Tukey's test.
To sum up, we found four numerical indicators that describe textual behavior of: (a) terms in school textbooks, (b) terms in scholarly articles, (c) textbook terms in nonspecific and popular science contexts, (d) scholarly terms in non-specific and popular science contexts. See Figure 7 for an example of results obtained for the Russian Language / Linguistics domain. As is seen from the diagram, semantic coherence of linguistic terms decreases as we move away from scholarly articles to school textbooks and, then, from textbooks and scholarly articles to non-specific and popular science contexts.
the two domains, especially as regards the choice of terms in the particular section of a subject domain. For more, refer to (Monakhov et al., 2022).
Figure 7. Comparison of cosine similarity measures as indicators of semantic coherence of terms from the Russian Language / Linguistics subject area in different domains
Рисунок 7. Сопоставление мер косинусной близости, указывающее на степень семантической спаянности терминов области знания «Русский язык / Лингвистика» в различных сферах употребления
Textbook terms in non-specific and
• popular science contexts
• Terms in school textbooks
Scholarly terms in non-specific and
popular science contexts
Terms in scholarly articles ♦
Т-1-1-1-1-1-1-1-г
0.28 0.30 0.32 0.34 0.36 0.38 0.40 0.42 0.44
The data analysis across different subject areas resulted in the following four groupings by type of established regularities:
0 < CSMdistance_textbooks_wiki < CSMdistance_articles_wiki <
CSMdistance_textbooks <
CSMdistance_articles <1 - Art, Geography, Computer Science, Musicology, Physical Education, Russian Language / Linguistics, Social Science;
0 < CSMdistance articles wiki <
CSMdistance_textbooks_wiki <
CSMdistance_articles <
CSMdistance_textbooks < 1 - Astronomy, History, Law, Literature;
0 < CSMdistance_articles <
CSMdistance_textbooks <
CSMdistance_articles_wiki <
CSMdistance_textbooks_wiki < 1 - Biology;
0 < CSMdistance_textbooks < CSMdistance_articles <
CSMdistance_textbooks_wiki <
CSMdistance_articles_wiki < 1 - Chemistry, Mathematics, Physics.
It seems logical that in most domains scholarly terms of the RusVectores model have higher average values of cosine similarity than schoolbook terms. This means that textbook terms have, in general, less potential for independent systematicity that scholarly terms. Another conclusion is more unexpected, while also more illustrative. To make it even more conspicuous, it is feasible to reduce the number of groupings from four to two through the merger of distance_textbooks_wiki with
distance_articles_wiki and distance_textbooks with distance_articles. Thus, we get:
0 < CSMdistance_
textbooks+articles_wiki <
CSMdistance_textbooks+articles <1 - Art, Geography, Computer Science, Musicology, Physical Education, Russian Language / Linguistics, Social Science, Astronomy, History, Law, Literature;
0 < CSMdistance_textbooks+articles < CSMdistance_textbooks+articles_wiki < 1 -Biology, Chemistry, Mathematics, Physics.
Thus, we distinguish between key subject areas in exact and natural sciences (Biology, Chemistry, Mathematics, Physics) and other disciplines belonging to arts and humanities. In the RusVectores model the terms from the first group have higher values of average cosine similarity than those in the second group. This indicates that terms from exact and natural sciences retain or even enhance their semantic similarity once they are used beyond scholarly articles or school textbooks. They tend to behave as a relatively cohesive semantic group. It would be fair to say that they resist the pressure of any other communication domain and harshly reject uncommon collocations limiting their functioning to recurrent contexts. In contrast, once they find themselves in a general linguistic context, terms from arts and humanities lose their similarity in contextual behavior and resemble a semantically dispersed cloud. They show more freedom in uncommon contexts and are quicker to transform into common words. These patterns found in the behavior of subject-specific terms used in uncommon contexts objectify the concept of "word familiarity". This concept, traditionally used to assess text complexity, has been often rejected as unreliable due to its subjective nature. Terms of exact and natural sciences tend to retain their semantic proofness as a group even beyond special or educational texts. This prevents their natural assimilation through semantic and communication tools. On the contrary, terms of arts and humanities are faster to become familiar, primarily due to the freedom of use that they show in diverse lexical contexts.
The cases of failure to meet the established pattern (compare, e.g., the data obtained for such subjects as Geography, Computer Science, and Astronomy vs. History, Law, and Literature) require further research that lies beyond the scope of the reported study. To comment just briefly, the reasons for numerical deviations in the overall patterns of term use may vary for different subjects. This may be due to comprehensive nature of a subject. Thus, geography combines elements of the natural sciences and humanities. Another reason is a small amount of information found in textbooks as certain subjects are only taught at a basic level, e.g., a school course in Astronomy. Finally, automatically extracted term lists for a particular subject may show considerable heterogeneity and include a big number of non-terms. In the latter case, the identified deviations from the established patterns are diagnostic in nature. They indicate the necessity of further improvement in automatic term extraction techniques and computer analysis of term functioning.
4. Conclusion
The toolkit used in the reported study to investigate the functioning of terminology is based on the principles of distributional semantics and the Word2Vec algorithms. It takes account of regular use of terms in similar lexical contexts. This creates the conditions to analyze contextual behavior of terms as elements of terminology systems based on semantically coherent groups of lexical units. This, in turn, allows to improve the results of statistical automatic term extraction from target corpora and analyze the behavior of terms in large volumes of text in different knowledge domains. The evidence for the reported study was taken from modern school textbooks. Due to the employed toolkit, we were able to compare the terminological load of textbooks in different subjects, i.e., the systemic use of groups of terms; to describe the structure of high-frequency non-terms; to explore the dynamics of new terms entering terminology systems within a set of educational and
methodological complexes, one of the courses or a specific textbook for a specific school grade; to compare through a range of metrics the regularities of term functioning in school textbooks, scholarly articles and non-specific contexts. The obtained results may be useful to experts in computer-assisted text analysis, general didactics, subject-specific teaching methodology and complexity studies.
Some of the study outcomes are in line with the established intuitive ideas about the use of subject-specific terminology in school textbooks. In some cases, however, these outcomes provide new insights. Thus, a common perception about the complexity and rigor of exact and natural sciences has been proved mathematically. Strikingly, these textual qualities are not simply due to the abundance of terminology, but, rather, due to rigid contextual and semantic coherence of terms that retains and even increases beyond the boundaries of their primary knowledge domain. On the other hand, commonly known for their more general descriptive character, textbooks in the Russian Language and Literature were found to be considerably different as they progressed from lower to upper secondary school. These differences concerned terminological load, systemic use of terms and the dynamics of new terms entering the textbooks.
Such factors as terminological load and the frequency of terms and non-terms contribute to the measure of school textbooks complexity. However, objective lexical indicators of complexity enter into complex and, at times, contradictory relations with both the measure of complexity and didactic principles of textbook efficiency. Thus, the study found lexical and thematic similarity in school textbooks for the Russian Language subject. As lexical diversity is one of the complexity-increasing factors, lexical and thematic similarity reduces both the measure of complexity and the text complexity in general. This facilitates the didactic principle of comprehensibility. At the same time, similarity undermines the motivation for learning and contradicts modern didactic
principles that require educational materials to be psychologically appropriate for students in terms of their age and individual characteristics. Irregular and contextually incoherent use of terms in a range of textbooks in arts and humanities decreases their measure of complexity. This, however, contradicts the didactic principles of continuity, consistency and systematicity of learning, which, on the opposite, increases the measure of complexity. Undoubtedly, follow-up research in lexical complexity of school textbooks should include the assessment of a textbook structure as well as the structure of an educational and methodological complex in general. The reason is that conclusions about the balance between complexity and difficulty are impossible to make without accounting for the dynamics of new terms entering textbooks as well as the relationship between the new and already familiar terms.
The reported results are only part of the study outcomes. Ultimately, the study aims to develop the Russian language terminological database relevant to the content of secondary education. Python codes developed specifically for the reported study can be reproduced with any other educational and methodological complex or a term-rich text corpus. All the study-related materials and outcomes including text corpora, term lists, program codes, word embedding models, graphs and semantic maps are available in the open-access scientific repository1.
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Все авторы прочитали и одобрили окончательный вариант рукописи.
All authors have read and approved the final manuscript.
Конфликты интересов: у авторов нет конфликтов интересов для декларации.
Conflicts of interests: the authors have no conflicts of interest to declare.
Sergei I. Monakhov, Ph.D. in Philology, Research Associate, Friedrich Schiller University Jena, Germany.
Сергей Игоревич Монахов, кандидат
филологических наук, научный сотрудник
Йенского университета им. Ф. Шиллера, Германия.
Vladimir V. Turchanenko, Junior Researcher, Institute of Russian Literature (Pushkinsky Dom) of the Russian Academy of Sciences, Saint Petersburg, Russia.
Владимир Владимирович Турчаненко,
младший научный сотрудник Института русской литературы (Пушкинский Дом) РАН, Россия.
Dmitrii N. Cherdakov, Senior Lecturer, Saint Petersburg University, Russia. Дмитрий Наилевич Чердаков, старший преподаватель Санкт-Петербургского
государственного университета, Россия.
