CC BY
38
Russian Journal of Linguistics
ISSN 2687-0088 (print), ISSN 2686-8024 (online)
2022 Vol. 26 No. 2 426-428
http://journals.rudn.ru/linguistics
https://doi.org/10.22363/2687-0088-30132
Research article
Text complexity and linguistic features: Their correlation in English and Russian
Dmitry A. MOROZOV1 0, Anna V. GLAZKOVA2 and Boris L. IOMDIN3
Novosibirsk State University, Novosibirsk, Russia 2University of Tyumen, Tyumen, Russia 3Vinogradov Russian Language Institute, RAS, Moscow, Russia [email protected]
Abstract
Text complexity assessment is a challenging task requiring various linguistic aspects to be taken into consideration. The complexity level of the text should correspond to the reader's competence. A too complicated text could be incomprehensible, whereas a too simple one could be boring. For many years, simple features were used to assess readability, e.g. average length of words and sentences or vocabulary variety. Thanks to the development of natural language processing methods, the set of text parameters used for evaluating readability has expanded significantly. In recent years, many articles have been published the authors of which investigated the contribution of various lexical, morphological, and syntactic features to the readability level. Nevertheless, as the methods and corpora are quite diverse, it may be hard to draw general conclusions as to the effectiveness of linguistic information for evaluating text complexity due to the diversity of methods and corpora. Moreover, a cross-lingual impact of different features on various datasets has not been investigated. The purpose of this study is to conduct a large-scale comparison of features of different nature. We experimentally assessed seven commonly used feature types (readability, traditional features, morphological features, punctuation, syntax frequency, and topic modeling) on six corpora for text complexity assessment in English and Russian employing four common machine learning models: logistic regression, random forest, convolutional neural network and feedforward neural network. One of the corpora, the corpus of fiction literature read by Russian school students, was constructed for the experiment using a large-scale survey to ensure the objectivity of the labeling. We showed which feature types can significantly improve the performance and analyzed their impact according to the dataset characteristics, language, and data source.
Keywords: text complexity, machine learning, neural network, corpus linguistics
© IDmitry A. Morozov, Anna V. Glazkova, Boris L. Iomdin, 2022
s -a ' This work is licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/
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For citation:
Morozov, Dmitry A., Anna V. Glazkova & Boris L. Iomdin. 2022. Text complexity and linguistic features: Their correlation in English and Russian. Russian Journal of Linguistics 26 (2). 426-448. https://doi.org/10.22363/2687-0088-30132
Научная статья
Сложность текста и лингвистические признаки: как они соотносятся в русском и английском языках
Д.А. МОРОЗОВ1 И, A.B. ГЛАЗКОВА2 , Б.Л. ИОМДИН3
1 Новосибирский государственный университет, Новосибирск, Россия 2Тюменский государственный университет, Тюмень, Россия
3Институт русского языка им. В. В. Виноградова РАН, Москва, Россия [email protected]
Аннотация
Автоматическая оценка читабельности текста — актуальная и непростая задача, которая требует учёта разнообразных лингвистических факторов. Сложность текста должна соответствовать уровню читателя: слишком сложный текст останется непонятым, слишком простой будет скучным. Исторически для оценки читабельности использовались простые характеристики: средняя длина слов и предложений, разнообразие лексики. Благодаря развитию методов обработки естественного языка набор используемых для оценки параметров текста существенно расширился. За последние годы было опубликовано множество работ, в которых исследовался вклад в сложность текста различных лексических, морфологических, синтаксических признаков. Тем не менее, поскольку использованные методы и корпусы довольно разнообразны, затруднительно делать общие выводы об эффективности различных лингвистических характеристик текста. Более того, не было проведено сравнение влияния признаков для различных языков. Целью настоящего исследования является проведение масштабного сравнения признаков различного характера. Мы экспериментально сравнили семь часто используемых типов признаков (индексы читабельности, традиционные, морфологические, синтаксические, пунктуационные, частотные признаки и тематическое моделирование) на материале трёх русскоязычных и трёх англоязычных корпусов, с использованием четырех распространённых алгоритмов машинного обучения: логистической регрессии, случайного леса, свёрточной нейронной сети и нейронной сети с прямой связью. Один из корпусов — корпус художественной литературы, читаемой российскими школьниками, — был создан для этого эксперимента с помощью масштабного опроса для обеспечения объективности разметки. Мы показали, какие типы признаков могут значительно повысить качество прогнозирования, и проанализировали их влияние в зависимости от характеристик корпуса, его языка и источника текстов.
Ключевые слова: сложность текста, машинное обучение, нейронные сети, корпусная лингвистика
Для цитирования:
Morozov D.A., Glazkova A.V., Iomdin B.L. Text complexity and linguistic features: Their correlation in English and Russian. Russian Journal of Linguistics. 2022. Vol. 26. № 2. P. 426-448. https://doi.org/10.22363/2687-0088-30132
1. Introduction
Text complexity is crucial for the comprehension process. Texts that are too difficult may be hard to understand. In contrast, unnecessarily simple texts may conflict with the reader's level of communicative skills. Hence, text complexity assessment is an essential task that represents a major challenge for developing natural language processing (NLP) tools. Text complexity can be expressed in different ways, ranging from quantitative characteristics to semantic complexity represented by text topics. Numerous studies have been published on evaluating various features for text complexity assessment. The reported results were obtained from text corpora of widely differing sizes and domains. Moreover, the authors used different machine learning (ML) models and text representation techniques (Feng et al. 2010, Ivanov et al. 2018, Cantos & Almela 2019, Isaeva & Sorokin 2020, Deutsch et al. 2020, Glazkova et al. 2021, Martinc et al. 2021). This makes it complicated to achieve an objective evaluation of the impact of different types of features.
The goal of this paper is to perform an extensive evaluation of seven feature types for text complexity assessment that were frequently used in research on the subject. The results allow us to make the text complexity analysis process more defined and transparent. These findings have a broad spectrum of potential applications in education and recommendation systems. We used the following feature types: readability, traditional features, morphological features, punctuation, syntax, frequency, and topic modeling. The features were evaluated on three Russian and three English text complexity corpora and four ML models in order to answer the following research questions (RQ).
• RQ1: How do different types of features affect the performance of baselines?
• RQ2: Is the impact of these feature types similar in English and Russian?
• RQ3: Do feature-enriched models outperform fine-tuned state-of-the-art language models?
This paper is organized as follows. Section 2 contains a brief review of related works. In Section 3, we introduce datasets and models utilized and provide some short background on the feature types we use. Section 4 presents and discusses the results. Section 5 concludes the paper.
2. Related Work
2.1. Readability: methods and approaches
The earliest approaches to automatic readability assessment, developed in the second half of the 20th century, were intuitive, severely limited by the small number
of existing natural language processing tools and the lack of computing power. Most of these readability indices represented linear combinations of simple features, such as average word or sentence length, the proportion of words in the text with a large number of syllables, and the proportion of words included in special lists of "simple" and "complex" words. A more detailed overview of these algorithms is given, for example, by Cantos and Almela (2019).
These algorithms have become widespread in practical tasks, despite their simplicity and seeming naivety. They are still in use in some spheres, including government requirements for insurance, for example, in some US states such as Connecticut (Chapter 699a. Readable language in insurance policies). At the same time, it is quite clear that such simple mechanisms cannot give a reliable result, especially in relation to fiction and poetry.
2.2. New possibilities: more features, more sophisticated models
The rapid development of NLP tools, including neural networks, has made it possible to significantly expand the set of features and to improve the quality of the text complexity assessment. Since the algorithm that solves this problem can be widely used in application-oriented studies, many authors have analyzed the impact of features of different nature (e.g. Feng et al. 2010, Ivanov et al. 2018, Cantos & Almela 2019, Isaeva & Sorokin 2020, Deutsch et al. 2020, Glazkova et al. 2021).
The most intuitive way to noticeably improve the quality of the prediction, which does not require serious modifications, is to use a combination of classical algorithms. Cantos and Almela (2019) analyzed this approach on a corpus containing excerpts from English-as-a-Foreign-Language textbooks. The presented classifier is based on features from Flesch-Kincaid readability test (Kincaid et al. 1975), Coleman-Liau index (Coleman & Liau 1975), Automated readability (ARI) index (Senter & Smith 1967), SMOG grade (McLaughlin 1969) and some other. The precision of the constructed algorithm significantly outperforms separate approaches.
At the same time, significant gains can be achieved using more abstract and complex characteristics. Feng et al. (2010) analyzed the impact of various categories of features on the complexity of the text, such as the number and density of named entities, semantic chains, referential relations, language modeling, syntactic dependencies, and morphology. Ivanov et al. (2018) considered 24 various features. such as average sentence and word lengths, word frequencies, morphological, and syntactic features on the Russian corpus.
The robustness of different features across various corpora with texts of different languages, styles, and genres is also a challenging question. This issue was partly solved by Isaeva and Sorokin (2020), who studied three groups of features, namely, average lengths plus frequencies, morphological, and syntactic ones. The experiments on three corpora of educational texts showed that there is a core of features that are crucial for all texts: the average number of syllables per word, the proportion of verbs in active voice among all words, the proportion of personal
pronouns among all words, and the average syntax tree depth. Deutsch et al. (2020) considered a few combinations of deep learning models with linguistically motivated features in order to determine how much such a combination will improve the quality of predictions.
As in many other areas of natural language processing, state-of-the-art results can be achieved by fine-tuning Bidirectional Encoder Representations from Transformers (BERT) presented by Devlin et al. (2019) and similar models. Martinc et al. (2021) studied unsupervised and supervised approaches, comparing BERT, Hierarchical attention networks, and Bidirectional Long short-term memory networks. The experiments were conducted on a few English and Slovenian corpora. The results suggested that BERT can be used as a high-level baseline for our research.
2.3. Russian datasets
The study of readability for the Russian language is developing more slowly than, for example, for English. This is largely due to the lack of text corpora with the labeled age of readers or readability level. The creation of a corpus of texts readable for students requires a large number of preliminary surveys of respondents of school age. This can be avoided by using the texts of school textbooks. For example, Ivanov et al. (2018) presented Russian Readability Corpus based on the texts of school textbooks on Social Studies for grades 5-11, later expanded by Isaeva and Sorokin (2020). An alternative way would be to use lists of Recommended Reading as done by Iomdin and Morozov (2021). Presumably, many school students read such texts. However, by their nature, such corpora are far from ideal: they contain texts that, in the opinion of adult compilers, should be read at the appropriate age, but they in no way guarantee the fact of reading, much less understanding of these texts. Such lists often include various historical texts that are incomprehensible at the level of an average student without additional explanations. In the above-mentioned list of recommended literature, there are such complex texts from the point of view of the reader as "The Lay of Igor's Campaign" and "The Tale of Bygone Years"1. Thus, despite the high labor intensity, a large-scale survey of schoolchildren seems to be the optimal way to select texts for the corpus, which makes it possible to achieve the best representativeness.
3. Datasets, features and models
3.1. Books Read by Students corpus
In real practice, text complexity is often identified with the age of the reader, usually a student. Thus, readability indices often predict a grade of school or college in which the text would be understandable (e.g., Kincaid et al. 1975). This makes it
1 It is interesting that due to the inability to take into account the obsolescence of the vocabulary, traditional indices assign these texts a very low level of complexity (Iomdin & Morozov 2021).
possible to exclude from consideration adult readers, whose reading experience can be incredibly variable and, as a result, can be estimated only with a very large margin of error. In our study, we decided to use this approximation and assess the age of a reader instead of the abstract complexity level.
To create a corpus with a labeled reading age, two stages of experiments have been conducted. At the first stage, respondents of preschool and school-age (or their parents, acting on their behalf) were asked to name a few of the books they had most recently read, together with their authors. The survey involved 1176 respondents under the age of 16, of which more than 1000 were of school age. In order to complete the list of presumably popular books, a similar additional survey was conducted at the linguistic session at the Sirius Educational Center (Russia), the target audience of the survey was mainly students of grades 9-11.
At the second stage, another group of respondents was asked to select the books they read from the list of those most frequently mentioned during the first stage, 93 books in total. The books were distributed over several subsurveys in such a way that each respondent was asked about no more than two books from the series. The experiment involved 1120 respondents, each of whom answered questions about 15 or 16 books.
Due to the insufficient number of respondents of primary school age and in order to exclude the peculiarities of the individual experience of readers, we decided to combine the ages of students into 5 categories: 1-2, 3-4, 5-7, 8-9, and 10-11 grades. The proportion of participants who read the text was calculated for each book and each age category. After that, the youngest category containing at least 25% of respondents who had read the book was assigned a book label. For example, "In Search of the Castaways" ("Les Enfants du capitaine Grant") was marked as read by 0% of respondents from the category 1-2, 13% of respondents from the category 3-4, and 70% of respondents in the category 5-7, thus, the category 5-7 was assigned.
As a result, 75 books were included into the Books Read by Students corpus (BR). A complete list is given in Appendix A. 18 texts from the second stage of the experiment did not receive any age label and were not included in the corpus. In such a situation, the level of complexity cannot be established even with a sharp increase in the proportion of readers when moving between age categories. A brief overview of the collected corpus is presented in Table 1.
Table 1. Brief statistics of the Books Read by Students corpus
Category All 1-2 3-4 5-7 8-9 10-11
Total texts 75 31 14 8 14 8
Total words 4077579 888984 881578 693448 1060079 553490
Total unique lemmas 50930 24513 24616 24185 30670 24645
Avg sentence length 9.41 8.37 9.58 11.19 9.55 10.44
Avg word length 4.94 4.88 5.02 5.06 4.91 4.83
Avg unique lemmas per text 6864.05 3431.6 6033.6 7375.6 6757.0 7375.6
3.2. Alternative datasets
As mentioned above, for the Russian language, there are few corpora with complexity labels. We decided to compare BR with two of them: Fiction Previews (Fic) presented by Glazkova et al. (2021) and Recommended Literature (RL), which we constructed in the previous study from the list of recommended literature for schoolchildren created by the Russian Ministry of Education (Iomdin & Morozov 2021). All collected texts were divided into fragments of 70 sentences each. This allowed us to considerably increase the size of datasets without significant loss of labeling quality (Isaeva & Sorokin 2020).
For English, there are a few corpora with complexity labels; we used three of them. Common Core State Standards (CC)2 is a corpus designed to represent text complexity levels for each grade in the USA. OneStopEnglish (OSE) corpus was specially created for training readability models (Vajjala & Lucic 2018). It consists of 189 text samples, each in three complexity versions. CommonLit (CL) corpus was presented at a Kaggle competition3. The main difference of this corpus from the rest ones is continuous labeling set instead of classes. An overview of the datasets is shown in Table 2.
Table 2. Some statistics of the datasets. BR — Books Read by Students, RL — Recommended Literature, Fic — Fiction Previews, CC — Common Core State Standards, CL — CommonLit, OSE — OneStopEnglish
Characteristics BR RL Fic CC CL OSE
Total texts 5795 9230 58184 219 2834 567
Total categories 5 3 2 6 1 3
Total words 2897003 4888290 26252666 84014 491944 381137
Total unique words 55577 103875 304731 10007 24449 13611
Avg words/text 984.75 1053.28 918.64 450.46 199.65 757.82
Avg words/sentence 13.92 14.95 13.12 22.24 24.94 22.04
Avg sentences/text 70 70 70 23.26 9.46 34.98
3.3. Linguistic Features
According to the related works, we identified seven types of features, which can be used to assess the text complexity: 1) readability indices, e.g., the Flesch-Kincaid readability test and the SMOG grade; 2) traditional features, e.g., the average word length and type/token ratio; 3) morphological feature, e.g., the proportion of nouns and verbs; 4) punctuation, e.g., the number of semicolons; 5) syntactic features, e.g., the average syntactic tree depth and number of clauses; 6) frequencies, e.g., the percentage of tokens included in the list of the most frequent words; and 7) topic modeling. In total, we collected 128 features for English and 126 for Russian of types 1-6. Additionally, we evaluated 100 topics using Latent Dirichlet allocation (Blei et al. 2003). To the best of our knowledge, such a wide
2 http://www.corestandards.org/
3 https://www.kaggle.eom/c/commonlitreadabilityprize
range of features was considered for the first time in relation to Russian text complexity models. A full list of features can be found in Appendix B.
For evaluation we used the following libraries: readability (Readability 0.3.1 2019), pymorphy2 (Korobov 2015), nltk (Loper & Bird 2002), gensim (Rehurek & Sojka 2010), spacy (Honnibal & Montani 2017), deeppavlov (Burtsev et al. 2018), and API of readability.io. The source code for our methods is available at GitHub (Readability 2021).
3.4. Models
We used the following machine learning algorithms as baselines:
1 Linear Support Vector Classifier (LSVC). LSVC was built with the l2 penalty and the squared hinge loss. We fitted LSVC on bag-of-words (BoW) text representations with a maximum length of 10000. Scikit-learn (Pedregosa et al. 2011) was used for implementation.
2 Random Forest (RF). We used 100 estimators and the Gini impurity to measure the quality of a split. The implementation details are the same as those for LSVC.
3 Feedforward Neural Network (FNN). The hyperparameters used are identified in Table 3. We employed the Adam optimizer (Kingma & Ba 2015). The model was implemented using Keras (Chollet et al. 2015). Each model was trained with early stopping for a maximum of 100 epochs and patience of 20. We utilized Sentence Transformers text representations obtained using the all-mpnet-base-v2 model (Reimers & Gurevych 2019) for the English corpora and the distiluse-base-multilingual-cased model (Reimers & Gurevych 2020) for the Russian ones.
4 Convolutional Neural Network (CNN). The training details are the same as for FNN. The model was implemented using FastText embeddings for English (Mikolov et al. 2018) and Russian (Kutuzov & Kuzmenko 2016).
Table 3. Hyperparameters for neural baselines
Hyperparameters FNN CNN
Number of convolutional layers - 2
Number of pooling layers - 2
Number of convolutional filters - 256
Filter size - 256
Number of fully connected layers 3 1
Size of fully connected layers 1024 32
Activation (hidden layers) Tanh relu
Activation (output layer) softmax (classification)
linear (regression)
Dropout 0.5
We randomly shuffled all the Russian corpora and the CL dataset and split them into train and test sets in the ratio of 3:1. The splitting was conducted in such a way that all fragments of one book belonged either to the train set or to the test
one. Due to the small number of documents in OSE and CC corpora, we used fivefold cross-validation on these datasets to obtain more reliable results. For all of the models above, we systematically evaluated each type of linguistic features applying the Min-Max technique for normalization.
To compare the scores obtained with the results of a few state-of-the-art models, we used BERT-base and RuBERT (Kuratov & Arkhipov 2019) for English and Russian corpora respectively. Each model was fine-tuned for 3 epochs using Transformers (Wolf et al. 2020) with the learning rate of 2e-5 using the AdamW optimizer (Loshchilov & Hutter 2018). We set batch size to 4 and maximum sequence size to 512. To validate our models during the development phase, we divided labelled data using the train and validation split in the ratio 90:10.
We used mean absolute error (MAE) and weighted F1-score to compare the results. MAE is calculated as an arithmetic average of the absolute errors et =yt —Xj, where yt is the prediction, xt is the true value, n is the number of values:
= ^i. (1)
n x /
The weighted F1-score calculates the standard F1-score for each label, and finds their average, weighted by the number of true instances for each label. The formula of the standard F1-score is:
2*Precision*Recall ^ . . TP „ .. TP
F1 =-, Precision =-,Kecaii =-, (2)
Precision+Recall TP+FP TP+FN v 7
where TP, FP, and FN are the numbers of true positive, false positive, and false negative predictions respectively.
4. Results and Discussion
We report the results in terms of the MAE (for the CL corpus) and weighted F1-score (for the other corpora) in Table 4. The gray highlight presents the values that outperform the baseline, the values that outperform the baseline by at least 1% are underlined. The best results are shown in bold. Appendix C contains the overall results expressed by several common metrics.
Based on the results, we can identify four performance categories, see Table 5, that describe the impact of various linguistic features (RQ1). In most cases, the considered features improved the model performance. Meanwhile, it was only morphological features that gave a positive impact in most classifiers for all corpora. Readability features exceeded the baseline on most models for most datasets except the BR corpus. Punctuation, traditional, and syntactic features showed a performance growth at least for two models on each corpus. Frequency and topic modeling features produced mixed results. On the one hand, topic modeling features improved the performance of all classifiers on two corpora. Nevertheless, the score on the OSE corpus increased for only RF classifier. This could be because the corpus contains parallel versions of the same papers. Although frequency features improved the performance in some cases, they demonstrated
higher MAE in most classifiers on the CL dataset. Probably, it reflects the fact that short texts normally lack word frequency and context information because of word sparsity (Yan et al. 2013, Xun et al. 2016).
Table 4. F1 (%) and MAE for each type of features. For F1 the best result is the highest, for MAE —
the lowest. BERT refers to the BERT-base in English corpora and to RuBERT in the Russian ones
Model BR RL Fic CC CL OSE
BERT 45.23 62.74 80.96 42.18 0.453 70.99
LSVC 32.31 63.16 76.66 28.22 0.673 70.41
RF 30.94 48.21 78.87 30.03 0.627 68.21
FNN 34.22 63.26 66.34 33.73 0.533 54.00
CNN 39.82 58.12 80.12 33.60 0.593 70.64
+ readability
LSVC 32.12 63.16 76.67 32.43 0.663 70.49
RF 29.19 49.89 78.45 27.77 0.599 70.11
FNN 40.89 63.62 68.23 37.56 0.502 56.07
CNN 45.90 61.35 80.52 35.89 0.590 68.59
+ traditional
LSVC 33.15 62.67 77.14 29.3 0.666 69.89
RF 30.03 46.53 78.26 28.57 0.609 73.01
FNN 32.12 69.76 70.51 34.7 0.482 58.76
CNN 44.32 65.19 80.68 45.98 0.604 64.82
+ morphological
LSVC 32.55 63.22 77.03 31.99 0.662 71.75
RF 30.36 46.63 76.20 29.56 0.611 70.67
FNN 35.63 69.12 72.04 37.42 0.504 62.00
CNN 42.29 68.63 80.75 37.12 0.573 69.02
+ punctuation
LSVC 32.26 62.87 76.73 30.44 0.664 70.41
RF 30.30 47.25 78.20 28.39 0.629 68.92
FNN 35.21 66.54 68.70 32.51 0.505 55.79
CNN 40.74 67.95 80.86 43.68 0.580 64.33
+ syntactic
LSVC 32.66 61.91 76.88 29.27 0.674 70.54
RF 28.84 46.70 77.41 33.97 0.617 72.59
FNN 32.10 69.41 68.31 36.48 0.476 56.68
CNN 45.49 65.35 81.01 36.19 0.592 58.71
+ frequency
LSVC 32.52 63.07 76.84 33.08 0.662 71.34
RF 30.01 45.87 77.76 26.02 0.640 67.63
FNN 31.45 67.46 67.58 35.33 0.729 63.01
CNN 46.97 65.08 81.11 38.65 0.597 56.38
+ topic modeling
LSVC 35.36 62.14 76.92 29.97 0.669 67.00
RF 34.09 49.44 77.65 27.15 0.623 66.10
FNN 38.85 62.01 77.30 34.08 0.516 59.46
CNN 43.93 65.78 80.91 41.28 0.588 64.95
Table 5. Features types with positive impact for N classifiers on each corpus. 1 — readability indices, 2 — traditional features, 3 — morphological features, 4 — punctuation, _5 — syntactic features, 6 — frequencies, 7 — topic modeling._
Improvement BR RL Fic CC CL OSE
N=4 7 - - 5 1, 3, 7 -
N=3 3 1, 3 1, 2, 3, 4, 5, 6, 7 1, 2, 3, 4, 6, 7 2, 4, 5 1, 3, 5
N=2 1, 2, 4, 5, 6 2, 4, 5, 6, 7 - 4 - 2, 4, 6
N=1 - - - - 6 7
Tables 6 and 7 illustrate the performance growth as a percentage averaged over all classifiers for Russian and English corpora (RQ2). The averaged results demonstrate that the models trained on Russian texts benefit more from topic modeling and frequency features in comparison with the models trained on English corpora. On the other hand, the results on the CC corpus indicate that this superiority is rather due to text length than language properties. Readability and punctuation features present similar results for both languages. Although morphological, traditional, and syntactic features show better performance on English texts, the results on specific corpora are strongly determined by the source of texts and the type of markup. Thus, any influence of syntactic features for the OSE corpus could not be identified during our experiments. However, there was a noticeable increase for the CC corpus containing fiction texts that characterized English as an analytic language. Overall, these results indicate that the impact of all feature types is mainly attributable to specific circumstances of a corpus. This enables one to use transfer-learning algorithms for cross-lingual analysis of text corpora having similar characteristics.
Table 6. Averaged performance growth for Russian corpora, %
Features BR RL Fic Avg Rus
Readability 7.13 2.4 0.71 3.41
Traditional 1.21 4.54 1.71 2.49
Morphological 2.3 6.04 1.62 3.32
Punctuation 0.75 4.91 0.93 2.2
Syntactic 0.58 4.26 0.63 1.83
Frequency 1.88 3.4 0.48 1.92
Topic modeling 10.87 3.04 4.07 5.99
Table 7. Averaged performance growth for English corpora, %
Features CC CL OSE Avg Eng
Readability 6.39 3.05 0.96 3.47
Traditional 9.67 3.04 -1.33 4.74
Morphological 8.3 3.19 4.51 5.33
Punctuation 7.2 2.06 -1.14 2.71
Syntactic 8.18 3.04 -1.33 3.3
Frequency 5.91 -9.54 -0.76 -1.46
The performance of the models trained on feature combinations per dataset is presented in Table 8. The results are given only for those models the performance
of which was increased by two and more types of features. We enriched the baseline models with the concatenation of all features that showed a positive impact for the relevant models and datasets. The combination of features increased the F1 of RF on the OSE corpus outperforming all the results obtained for this dataset. This result is marked with an asterisk (*). Moreover, FNN trained on feature combinations showed the best result among all the feature-enriched models on the CL corpus. Taken together, the results presented in Table 4 and Table 8 demonstrate that feature-enriched models outperformed BERT on five out of the six corpora (RQ3). In some cases, significant increases were obtained, including 7.02% for the RL corpus and 3.8% for the CC corpus. By contrast, the performance of feature-enriched models depends on the features used and data specifics. Simultaneously, in some cases, models trained on feature combination showed a worse result, than those trained on the one type of features.
Table 8. F1 (%) and MAE for feature combinations
Model BR RL Fic CC CL OSE
LSVC 34.50 - 78.09 33.12 0.633 71.44
RF - 49.38 - - 0.568 76.44*
FNN 40.88 62.99 78.70 39.71 0.466 74.24
CNN 43.85 65.29 81.06 43.58 0.541 -
BERT 45.23 62.74 80.96 42.18 0.453 70.99
5. Conclusion
We have presented the first comparative analysis of the impact of seven types of linguistic features on the performance of text complexity models. We provided the results of large-scale experiments on six text corpora. Each feature type was evaluated in four representative ML models. Our research demonstrated the advantage of some features over others. For example, morphological features improved the performance of our models in almost all cases. At the same time, topic modeling features did not show any positive impact on the corpus containing parallel versions of the same papers. We also identified performance categories based on the scores obtained and estimated the impact of feature combinations. According to our study, the results depend more on the specific characteristics of the dataset than on language. This provides an opportunity for exploring cross-lingual transfer learning and multilingual models for text complexity assessment. Finally, experimental results on five out of the six corpora showed that feature-enriched models can achieve significant improvements in comparison with the state-of-the-art ones. Here, future research may focus on evaluating more complex semantic and narrative features, such as plot characteristics and the features related to named entity analysis, on including BERT-based features, and on explaining text complexity in terms of each feature type.
Acknowledgements
The article was funded by RFBR, project number 19-29-14224.
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Appendix A. Books Read by Students corpus
If the original text was published in a language other than English, the translated title is
followed by the title in the original language.
Table 9. Books included into the Books Read by Students corpus
Category Title (original title) Author(s)
1-2 Winnie-the-Pooh Alan Alexander Milne
1-2 The Wizard of the Emerald City (Волшебник Изумрудного города) Alexander Volkov
1-2 Urfin Jus and his Wooden Soldiers (Урфин Джус и его деревянные солдаты) Alexander Volkov
1-2 The Smart Dog Sonya (Умная собачка Соня) Andrey Usachev
1-2 Grandma and Eight Children in the Forest (Mormor og de atte ungene i skogen) Anne-Catharina Vestly
1-2 Eight Children and a Truck (Atte sma, to store og en lastebil) Anne-Catharina Vestly
1-2 Pippi Longstocking (Pippi Langstrump) Astrid Lindgren
1-2 Emil of Lonneberga (Emil i Lonneberga) Astrid Lindgren
1-2 The Lion, the Witch and the Wardrobe Clive Staples Lewis
1-2 Harry Potter and the Half-Blood Prince Joanne Rowling
Category Title (original title) Author(s)
1-2 Harry Potter and the Chamber of Secrets Joanne Rowling
1-2 Harry Potter and the Philosopher's Stone Joanne Rowling
1-2 Alice's Adventures in Wonderland Lewis Carroll
1-2 Waffle Hearts (Vaffelhjarte) Maria Parr
1-2 Dunno in Sun City (Незнайка в Солнечном городе) Nikolay Nosov
1-2 The Adventures of Dunno and his Friends (Приключения Незнайки и его друзей) Nikolay Nosov
1-2 The Little Witch (Die kleine Hexe) Otfried Preußler
1-2 Treasure Island Robert Louis Stevenson
1-2 The Wonderful Adventures of Nils (Nils Holgerssons underbara resa genom Sverige) Selma Lagerlöf
1-2 Pancakes for Findus (Pannkakstartan) Sven Nordqvist
1-2 When Findus was Little and Disappeared (Nar Findus var liten och forsvann) Sven Nordqvist
1-2 The Mechanical Santa (Tomtemaskinen) Sven Nordqvist
1-2 Findus and the Fox (Ravjakten) Sven Nordqvist
1-2 Findus Plants Meatballs (Kackel i gronsakslandet) Sven Nordqvist
1-2 Findus Goes Fishing (Stackars Pettson) Sven Nordqvist
1-2 Findus Goes Camping (Pettson taltar) Sven Nordqvist
1-2 Findus at Christmas (Pettson far julbesok) Sven Nordqvist
1-2 Findus Moves Out (Findus flyttar ut) Sven Nordqvist
1-2 The Rooster's Minute (Tuppens minut) Sven Nordqvist
1-2 Finn Family Moomintroll (Trollkarlens hatt) Tove Jansson
1-2 The Adventures of Dennis (Денискины рассказы) Viktor Dragunsky
3-4 Seven Underground Kings (Семь подземных королей) Alexander Volkov
3-4 Ronia, the Robber's Daughter (Ronja rovardotter) Astrid Lindgren
3-4 Robinson Crusoe Daniel Defoe
3-4 Pollyanna Eleanor H. Porter
3-4 Three Jolly Fellows (Naksitrallid) Eno Raud
3-4 Harry Potter and the Deathly Hallows Joanne Rowling
3-4 Harry Potter and the Goblet of Fire Joanne Rowling
3-4 Harry Potter and the Prisoner of Azkaban Joanne Rowling
3-4 The Mysterious Island Jules Verne
3-4 One hundred years ahead (Сто лет тому вперёд) Kir Bulychev
3-4 The Adventures of Tom Sawyer Mark Twain
3-4 The Little Water Sprite (Der kleine Wassermann) Otfried Preußler
3-4 The Little Ghost (Das kleine Gespenst) Otfried Preußler
3-4 Comet in Moominland (Kometjakten) Tove Jansson
5-6-7 The Three Musketeers (Les Trois Mousquetaires) Alexandre Dumas
5-6-7 The Captain's Daughter (Капитанская дочка) Alexander Pushkin
5-6-7 The Adventure of the Final Problem Arthur Conan Doyle
5-6-7 The Hound of the Baskervilles Arthur Conan Doyle
5-6-7 A Study in Scarlet Arthur Conan Doyle
5-6-7 The Hobbit, or There and Back Again John Ronald Reuel Tolkien
5-6-7 Harry Potter and the Order of the Phoenix Joanne Rowling
5-6-7 In Search of the Castaways (Les Enfants du capitaine Grant) Jules Verne
8-9 The Time Is Always Good (Время всегда хорошее) Andrey Zhvalevsky and Evgeniya Pasternak
Category Title (original title) Author(s)
8-9 Monday Begins on Saturday (Понедельник начинается в субботу) Arkady and Boris Strugatsky
8-9 The Lost World Arthur Conan Doyle
8-9 His Last Bow Arthur Conan Doyle
8-9 The Sign of the Four Arthur Conan Doyle
8-9 The Adventure of the Empty House Arthur Conan Doyle
8-9 The Adventure of the Six Napoleons Arthur Conan Doyle
8-9 Ivanhoe Walter Scott
8-9 The Two Captains (Два капитана) Veniamin Kaverin
8-9 The Invisible Man H.G. Wells
8-9 The Lord of the Rings John Ronald Reuel Tolkien
8-9 George's Secret Key to the Universe Lucy Hawking, Stephen Hawking, Christophe Galfard
8-9 The Master and Margarita (Мастер и Маргарита) Mikhail Bulgakov
8-9 Dandelion Wine Ray Bradbury
10-11 The Catcher in the Rye J. D. Salinger
10-11 1984 George Orwell
10-11 Fathers and Sons (Отцы и дети) Ivan Turgenev
10-11 Brave New World Aldous Huxley
10-11 Fahrenheit 451 Ray Bradbury
10-11 Lord of the Flies William Golding
10-11 Crime and Punishment (Преступление и наказание) Fyodor Dostoevsky
10-11 To Kill a Mockingbird Harper Lee
Appendix B. Evaluated Features Readability indices
1 Flesch-Kincaid readability test (Kincaid et al. 1975).
2 Coleman-Liau index (Coleman and Liau 1975).
3 Automated readability (ARI) index (Senter and Smith 1967).
4 SMOG grade (McLaughlin 1969).
5 Dale-Chall index (Dale and Chall 1948).
Traditional features
1 Average and mean sentence length.
2 Average and mean word length.
3 Long words (>4 syllables) proportion.
4 Type/token ratio (TTR) (Templin 1957).
5 NAV: TTR for Nouns only plus TTR for Adjectives only divided by TTR for Verbs only (Solnyshkina et al. 2018).
Morphological features
1 Percentages of lexical categories.
2 Percentage of grammatical cases.
3 Proportion of animated nouns.
4 Proportion of grammatical aspects for verbs.
5 Proportion of grammatical tenses for verbs.
6 Proportion of transitive verbs.
Punctuation
1 Punctuation/token ratio.
2 Semicolons/token ratio.
Syntactic features
Three features were extracted from each of the following characteristics: average, mean, and maximum.
1 Syntactic tree depth.
2 Distance between a node and its descendant.
3 Number of clauses.
4 Number of adverbial clause modifiers.
5 Number of adnominal clauses.
6 Number of clausal complements.
7 Number of open clausal complements.
8 Number of nominal modifiers.
9 Length of nominal modifiers sequence.
Frequencies
For evaluating frequencies of Russian and English words we used dictionaries based on Russian National Corpus (Lyashevskaya & Sharoff 2009) and British National Corpus (Leech et al. 2001) respectively.
1 Average and mean frequency.
2 Proportion of words, which are in the list of the most 100/200/.../1000 popular words, and similar features for nouns, verbs, adverbs, and adjectives separately.
Appendix C. Overall Results
In the tables below we use the following notation: F — F1-score weighted, P — precision weighted, R — recall weighted, MAE — mean absolute error, MSE — mean squared error.
MSE measures the average of the squares of the errors:
MS£ = SU (rt-ft), (3)
n
where Yt is the vector of true values, ^ is the vector of predicted values.
Russian corpora
_Table 10. Results for the Books Read By Students corpus_
Model F P R
BERT 45.23 54.06 41.32
LSVC 32.31 35.74 34.28
RF 30.94 32.73 37.18
FNN 34.22 39.06 31.75
CNN 39.82 57.34 33.66
F P R F P R
+ readability + traditional
LSVC 32.12 35.5 34.2 33.15 36.79 35.2
RF 29.19 26.87 36.04 30.03 32.49 36.34
FNN 40.89 61.23 31.83 32.12 44.37 27.08
CNN 45.9 66.18 37.8 44.32 64.88 36.27
+ morphological + punctuation
LSVC 32.55 37.52 36.5 32.26 35.79 34.35
RF 30.36 37.52 36.5 30.3 37.94 36.57
FNN 35.63 42.75 31.68 35.21 39.54 33.05
CNN 42.29 55.72 37.26 40.74 57.25 33.44
+ syntactic + frequency
LSVC 32.66 36.02 34.66 32.52 35.77 34.28
RF 28.84 31.26 34.74 30.01 32.14 35.88
FNN 32.1 40.95 28.46 31.45 37.54 28.39
CNN 45.49 67.47 36.57 46.97 69.57 38.41
+ topic modeling Combined
LSVC 35.36 38.63 36.88 34.5 37.36 35.88
RF 34.09 37.74 38.18 - - -
FNN 38.85 45.77 35.96 40.88 55.03 35.96
CNN 43.93 62.93 36.65 43.85 62.93 37.18
Table 11. Results for the Recommended Literature corpus
Model F P R
BERT 62.74 65.71 61.86
LSVC 63.16 63.54 64.98
RF 48.21 61.8 59.92
FNN 63.26 79.19 53.76
CNN 58.12 58.23 58.99
F P R F P R
+ readability + traditional
LSVC 63.16 63.22 64.89 62.67 62.83 64.56
RF 49.89 63.88 60.68 46.53 55.2 58.82
FNN 63.62 81.66 52.91 69.76 93.52 56.03
CNN 61.35 66.33 59.49 65.19 66.22 64.64
+ morphological + punctuation
LSVC 63.22 63.11 64.98 62.87 63.07 64.73
RF 46.63 58.54 59.07 47.25 62.9 59.58
FNN 69.12 92.34 55.78 66.54 87.1 54.43
CNN 68.63 72.84 66.58 67.95 71.19 66.33
+ syntactic + frequency
LSVC 61.91 61.88 63.88 63.07 62.93 64.64
RF 46.7 57.58 58.9 45.87 57.89 58.65
FNN 69.41 93.01 55.78 67.46 89.35 54.6
CNN 65.35 69.58 63.21 65.08 66.22 64.64
+ topic modeling Combined
LSVC 62.14 62.71 64.22 - - -
RF 49.44 65.98 60.68 49.38 62.93 60.34
FNN 62.01 65.98 59.66 62.99 68.99 58.99
CNN 65.78 67.24 64.89 65.29 68.18 63.54
Table 12. Results for the Fiction Previews corpus
Model F P R
BERT 80.96 81.83 80.82
LSVC 76.66 77.89 76.87
RF 78.87 79.67 78.99
FNN 66.34 72.31 65.01
CNN 80.12 80.87 80.04
F P R F P R
+ readability + traditional
LSVC 76.67 77.84 76.87 77.14 78.29 77.34
RF 78.45 78.85 78.51 78.26 78.86 78.36
FNN 68.23 72.54 67.36 70.51 70.61 70.49
CNN 80.52 81.9 80.37 80.68 81.74 80.56
+ morphological + punctuation
LSVC 77.03 78.27 77.24 76.73 77.94 76.94
RF 76.2 77.16 76.38 78.2 78.93 78.32
FNN 72.04 72.09 72.04 68.7 68.75 68.69
CNN 80.75 81.73 80.65 80.86 81.84 80.75
+ syntactic + frequency
LSVC 76.88 78.08 77.09 76.84 78 77.04
RF 77.41 78.21 77.54 77.76 78.4 77.86
FNN 68.31 68.41 68.29 67.58 67.59 67.57
CNN 81.01 81.97 80.9 81.11 82.08 81.01
+ topic modeling Combined
LSVC 76.92 78.18 77.12 78.09 79.3 78.27
RF 77.65 78 77.71 - - -
FNN 77.3 78.28 77.17 78.7 79.06 78.66
CNN 80.91 82.07 80.78 81.06 82.17 80.93
English corpora
Table 13. Results for the Common Core State Standards corpus
Model F P R
BERT 42.18 64.57 33.77
LSVC 28.22 30.13 30.61
RF 30.03 30.38 34.65
FNN 33.73 37.93 32.9
CNN 33.6 58.04 26.92
F P R F P R
+ readability + traditional
LSVC 32.43 33.55 35.59 29.3 31.37 31.5
RF 27.77 26.95 31.95 28.57 28.68 32.88
FNN 37.56 42.34 36.53 34.7 38.48 34.28
CNN 35.89 56.83 29.25 45.98 78.2 36.12
+ morphological + punctuation
LSVC 31.99 35.29 33.33 30.44 32.07 33.32
RF 29.56 29.53 34.26 28.39 27.23 34.65
FNN 37.42 46.15 34.7 32.51 37.2 32
CNN 37.12 57.32 30.62 43.68 60.51 37.95
+ syntactic + frequency
LSVC 29.27 29.45 31.95 33.08 35.74 34.67
RF 33.97 34.75 38.33 26.02 23.17 31.55
FNN 36.48 41.42 35.64 35.33 40.79 34.27
CNN 36.19 62.18 28.3 38.65 54.04 32.45
+ topic modeling Combined
LSVC 29.97 31.38 32.42 33.12 35.21 34.67
RF 27.15 29.19 30.15 - - -
FNN 34.08 38.34 32.91 39.71 47.55 37.94
CNN 41.28 65.93 33.85 43.58 44.09 39.44
Table 14. Results for the CommonLit corpus
Model MAE MSE
BERT 0.4532 0.3159
LSVC 0.6728 0.695
RF 0.6266 0.6199
FNN 0.533 0.4421
CNN 0.5926 0.555
MAE MSE MAE MSE
+ readability + traditional
LSVC 0.6627 0.6742 0.6664 0.6819
RF 0.5986 0.5743 0.609 0.5831
FNN 0.5024 0.4045 0.4823 0.3832
CNN 0.5896 0.5496 0.6041 0.5813
+morphological + punctuation
LSVC 0.6621 0.6775 0.664 0.6785
RF 0.6113 0.5917 0.6288 0.6204
FNN 0.5042 0.4002 0.5053 0.4102
CNN 0.5728 0.5269 0.5803 0.5307
+ syntactic + frequency
LSVC 0.6741 0.6924 0.6619 0.6703
RF 0.6167 0.5853 0.6401 0.643
FNN 0.4759 0.3705 0.7293 0.7627
CNN 0.5923 0.5566 0.5973 0.5602
+ topic modeling combined
LSVC 0.6686 0.6861 0.6334 0.6166
RF 0.623 0.5986 0.568 0.5174
FNN 0.5156 0.4149 0.4658 0.3542
CNN 0.5882 0.5403 0.5408 0.4726
Table 15. Results for the OneStopEnglish corpus
Model F P R
BERT 70.99 78.15 69.34
LSVC 70.41 72.15 72.03
RF 68.21 70.44 69.85
FNN 54 56.34 52.83
CNN 70.64 84.44 65.23
F P R F P R
+ readability + traditional
LSVC 70.49 72.17 72.02 69.89 71.76 71.69
RF 70.11 71.63 71.83 73.01 74.89 74.45
FNN 56.07 59.02 54.59 58.76 62.86 57.18
CNN 68.59 76.29 67.37 64.82 77.32 60.71
+ morphological + punctuation
LSVC 71.75 73.65 73.39 70.41 72.15 72.03
RF 70.67 72.22 72.25 68.92 70.24 70.4
FNN 62 65.37 60.19 55.79 57.56 54.8
CNN 69.02 78.87 66.33 64.33 75.55 60.33
+ syntactic + frequency
LSVC 70.54 72.61 72.37 71.34 73.1 73.04
RF 72.59 73.67 73.82 67.63 68.8 69.89
FNN 56.68 77.87 49.85 63.01 65.63 61.68
CNN 58.71 73.85 54.88 56.38 68.41 53.15
+ topic modeling Combined
LSVC 67 68.9 69.14 71.44 72.96 73.07
RF 66.1 68.1 66.45 76.44 77.18 77.37
FNN 59.46 61.84 58.38 74.24 75.71 74.17
CNN 64.95 76.98 62.17 - - -
Article history:
Received: 20 October 2021 Accepted: 21 Jenuary 2022
Bionotes:
Dmitry A. MOROZOV is Junior Researcher at the Laboratory of Applied Digital Technologies, International Mathematical Center of Novosibirsk State University, Novosibirsk, Russia, and Developer at the Russian National Corpus. His spheres of interest include corpus linguistics, discrete math, math modeling, and machine learning. Contact information:
Novosibirsk National Research State University 1 Pirogova, Novosibirsk, 630090, Russia e-mail: [email protected] ORCID: 0000-0003-4464-1355
Anna V. GLAZKOVA is Doctor of Sc. (Technology), Associate Professor of the
Department of Software at the Institute of Mathematics and Computer Science of Tyumen
University, Russia. Her current research interests include natural language processing and
text mining, with a focus on text classification and deep learning.
Contact information:
University of Tyumen
6 Volodarsky, Tyumen, 625003, Russia
e-mail: [email protected]
ORCID: 0000-0001-8409-6457
Boris L. IOMDIN holds a Ph.D. in Philology and is a Leading Researcher at Vinogradov Russian Language Institute of the Russian Academy of Sciences, Moscow, Russia. He is one of the authors and editors of the Active Dictionary of the Russian Language and a co-author of the project "Semantic Complexity of Words". Contact information:
Vinogradov Russian Language Institute of the Russian Academy of Sciences 18/2 Volkhonka, Moscow, 119019, Russia e-mail: [email protected] ORCID: 0000-0002-1767-5480
Сведения об авторах:
Дмитрий Алексеевич МОРОЗОВ - младший научный сотрудник Лаборатории прикладных цифровых технологий Международного математического центра НГУ, Новосибирск, Россия. Сотрудник Национального корпуса русского языка. Область научных интересов: корпусная лингвистика, дискретная математика, математическое моделирование, машинное обучение. Контактная информация:
Новосибирский национальный исследовательский государственный университет Россия, 630090, Новосибирск, ул. Пирогова, д. 1 e-mail: [email protected] ORCID: 0000-0003-4464-1355
Анна Валерьевна ГЛАЗКОВА - кандидат технических наук, доцент кафедры программного обеспечения Института математики и компьютерных наук Тюменского государственного университета, Тюмень, Россия. Область научных интересов: обработка естественного языка, интеллектуальный анализ текста, классификация текстов, глубокое обучение. Контактная информация:
Тюменский государственный университет Россия, 625003, Тюмень, ул. Володарского, д. 6 e-mail: [email protected] ORCID: 0000-0001-8409-6457
Борис Леонидович ИОМДИН - кандидат филологических наук, ведущий научный сотрудник Института русского языка им. В.В. Виноградова Российской академии наук, Москва, Россия. Один из авторов и редакторов Активного словаря русского языка, соавтор проекта «Семантическая сложность слов». Контактная информация:
Институт русского языка им. В. В. Виноградова РАН Россия, 119019, Москва, ул. Волхонка, д. 18/2 e-mail: [email protected] ORCID: 0000-0002-1767-5480
