Complexity Of Grammars Research Articles

Understanding Chichewa Noun Classes and Morphological Structure

This paper explores the complex noun classification system of Chichewa, a Bantu language primarily spoken in Malawi and surrounding regions, with approximately 15 million speakers. This system, which categorizes nouns based on both semantic and morphological criteria, profoundly impacts subject-verb agreement and concord usage, thereby shaping Chichewa sentence structure. Nouns are divided into various classes, each marked by specific prefixes that change with singular and plural forms. For example, class 5 and 6 nouns typically begin with consonants such as b-, d-, and dz-, with singular forms prefixed by "li-" and plural forms by "ma-". A distinctive feature of some class 6 nouns is their retention of the "ma-" prefix in both singular and plural contexts. The paper also discusses classes 7 and 8, which differentiate nouns starting with "ch-" in singular and "z-" in plural, and classes 9 and 10, where nouns generally maintain the same form between singular and plural, using "i-" for singular and "zi-" for plural. Diminutive prefixes "ka-" and "ti-" classify nouns into classes 12 and 13, while nouns beginning with "u-" or forming plurals with "ma-" are assigned to classes 14 and 6, respectively. The study further explores locative prefixes, with class 15 involving gerund forms with "ku- ", class 16 using "pa-" for "on", class 17 using "ku-" for "at", and class 18 using "mu-" for "in". These classifications not only highlight the morphological complexity and systematic nature of Chichewa grammar and syntax but also provide a comprehensive framework for understanding and effectively using the language, offering valuable insights for linguists, language learners, and educators.

Bridging Linguistic Gaps: Developing a Greek Text Simplification Dataset

Text simplification is crucial in bridging the comprehension gap in today’s information-rich environment. Despite advancements in English text simplification, languages with intricate grammatical structures, such as Greek, often remain under-explored. The complexity of Greek grammar, characterized by its flexible syntactic ordering, presents unique challenges that hinder comprehension for native speakers, learners, tourists, and international students. This paper introduces a comprehensive dataset for Greek text simplification, containing over 7500 sentences across diverse topics such as history, science, and culture, tailored to address these challenges. We outline the methodology for compiling this dataset, including a collection of texts from Greek Wikipedia, their annotation with simplified versions, and the establishment of robust evaluation metrics. Additionally, the paper details the implementation of quality control measures and the application of machine learning techniques to analyze text complexity. Our experimental results demonstrate the dataset’s initial effectiveness and potential in reducing linguistic barriers and enhancing communication, with initial machine learning models showing promising directions for future improvements in classifying text complexity. The development of this dataset marks a significant step toward improving accessibility and comprehension for a broad audience of Greek speakers and learners, fostering a more inclusive society.

When disability and homoparenting meet: the adoption of children with disability by same sex couples.

The present theoretical essay is based on six reports concerning same-sex couples and gay and lesbian people in order to interconnect homoparenting and the adoption of children with disabilities, through the lenses of human and social sciences in public health. The reports were interpreted in light of studies on same-sex adoption and the adoption of children with disabilities. Feminist approaches related to care and disability were also included in the interpretative perspective, operating as expressive webs of grammars of ableism. It was found that media approaches endorse the right to family formation and the adoption of children with disabilities by homoparental families, but with little critical depth on the category of disability and without highlighting support for the adoption of all adoptee profiles. Moreover, the intersections between homophobia and ableism increase discriminatory and oppressive logics, with the union of social groups considered to be "undesirable" representing a strategy of governmentality that reveals the complexity of grammars of ableism, applied to the sexual and reproductive rights of LGBTQIA+ adopters and to the fundamental rights of children and adolescents with disabilities who are available for adoption.

Chinese Clinical Named Entity Recognition From Electronic Medical Records Based on Multisemantic Features by Using Robustly Optimized Bidirectional Encoder Representation From Transformers Pretraining Approach Whole Word Masking and Convolutional Neural Networks: Model Development and Validation

Clinical electronic medical records (EMRs) contain important information on patients' anatomy, symptoms, examinations, diagnoses, and medications. Large-scale mining of rich medical information from EMRs will provide notable reference value for medical research. With the complexity of Chinese grammar and blurred boundaries of Chinese words, Chinese clinical named entity recognition (CNER) remains a notable challenge. Follow-up tasks such as medical entity structuring, medical entity standardization, medical entity relationship extraction, and medical knowledge graph construction largely depend on medical named entity recognition effects. A promising CNER result would provide reliable support for building domain knowledge graphs, knowledge bases, and knowledge retrieval systems. Furthermore, it would provide research ideas for scientists and medical decision-making references for doctors and even guide patients on disease and health management. Therefore, obtaining excellent CNER results is essential. We aimed to propose a Chinese CNER method to learn semantics-enriched representations for comprehensively enhancing machines to understand deep semantic information of EMRs by using multisemantic features, which makes medical information more readable and understandable. First, we used Robustly Optimized Bidirectional Encoder Representation from Transformers Pretraining Approach Whole Word Masking (RoBERTa-wwm) with dynamic fusion and Chinese character features, including 5-stroke code, Zheng code, phonological code, and stroke code, extracted by 1-dimensional convolutional neural networks (CNNs) to obtain fine-grained semantic features of Chinese characters. Subsequently, we converted Chinese characters into square images to obtain Chinese character image features from another modality by using a 2-dimensional CNN. Finally, we input multisemantic features into Bidirectional Long Short-Term Memory with Conditional Random Fields to achieve Chinese CNER. The effectiveness of our model was compared with that of the baseline and existing research models, and the features involved in the model were ablated and analyzed to verify the model's effectiveness. We collected 1379 Yidu-S4K EMRs containing 23,655 entities in 6 categories and 2007 self-annotated EMRs containing 118,643 entities in 7 categories. The experiments showed that our model outperformed the comparison experiments, with F1-scores of 89.28% and 84.61% on the Yidu-S4K and self-annotated data sets, respectively. The results of the ablation analysis demonstrated that each feature and method we used could improve the entity recognition ability. Our proposed CNER method would mine the richer deep semantic information in EMRs by multisemantic embedding using RoBERTa-wwm and CNNs, enhancing the semantic recognition of characters at different granularity levels and improving the generalization capability of the method by achieving information complementarity among different semantic features, thus making the machine semantically understand EMRs and improving the CNER task accuracy.

OVERCOMING CHALLENGES: TEACHING RUSSIAN LANGUAGE TO UZBEK STUDENTS AT THE PHILOLOGICAL INSTITUTE

This article discusses the difficulties faced by Uzbek students at the Philological Institute when learning the Russian language. Specifically, it focuses on five key challenges: the Cyrillic alphabet, pronunciation and phonetics, grammar complexity, vocabulary expansion, and cultural context. The article provides strategies and techniques that teachers can employ to overcome these challenges and facilitate effective language learning for Uzbek students

Genre Analysis of Research Abstract: A Literature Review

The presence of an abstract is required in a research publication, and it is a recurring problem for graduate students. The purpose of this literature review article is to elucidate on the following topics by evaluating relevant research publications published in respectable journals between 2014 and 2021: (1) tendency of rhetorical move usage, (2) linguistic features usage in rhetorical moves, and (3) pedagogical implications of rhetorical moves on teaching writing. In previous studies, the following conclusions were confirmed:(1) rhetorical move usage is categorized as obligatory moves and supplementary moves, (2) linguistic features usage in rhetorical moves includes grammatical tense usage, voice form usage, metadiscourse marker, and grammar complexity, and (3) In several research, rhetorical moves yielded positive effects in teaching writing, especially for academic writing instruction and research. This study recommends that more extensive research be conducted, concentrating not just on the moves and steps but also on genre development and their implications. Furthermore, further research is expected to gain a deeper investigation into how genre analysis can improve one’s writing quality. Keywords: genre analysis, rhetorical moves, research abstract.

A variationist perspective on the comparative complexity of four registers at the intersection of mode and formality

Abstract In this paper, we operationalize register differences at the intersection of formality and mode, and distinguish four broad register categories: spoken informal (conversations), spoken formal (parliamentary debates), written informal (blogs), and written formal (newspaper articles). We are specifically interested in the comparative probabilistic/variationist complexity of these registers – when speakers have grammatical choices, are the probabilistic grammars regulating these choices more or less complex in particular registers than in others? Based on multivariate modeling of richly annotated datasets covering three grammatical alternations in two languages (English and Dutch), we assess the complexity of probabilistic grammars by drawing on three criteria: (a) the number of constraints on variant choice, (b) the number of interactions between constraints, and (c) the relative importance of lexical conditioning. Analysis shows that contrary to theorizing in variationist sociolinguistics, probabilistic complexity differences between registers are not quantitatively simple: formal registers are consistently the most complex ones, while spoken registers are the least complex ones. The most complex register under study is written-formal quality newspaper writing. We submit that the complexity differentials we uncover are a function of acquisitional difficulty, of on-line processing limitations, and of normative pressures.

The complexity of trees, universal grammar and economy conditions

In this squib, I argue that the child faces a severe computational complexity problem in parsing even the simplest of trees: the number of possible trees consistent with UG grows exponentially as a function of the number of lexical items. Economy conditions have the result of drastically decreasing the complexity of the parsing task. I also discuss the relationship between UG, I-language, economy conditions and explanatory adequacy.

RETHINKING THE FUNCTIONAL EXPANSION OF QUASI-DOUBLE-VALENCY VERBS FROM THE CHANGE OF SYNTACTIC VALENCE -- AN IN-DEPTH ANALYSIS BASED ON THE CHANGE OF EMOTIONAL BEHAVIOR AND COGNITIVE PSYCHOLOGY

RETHINKING THE FUNCTIONAL EXPANSION OF QUASI-DOUBLE-VALENCY VERBS FROM THE CHANGE OF SYNTACTIC VALENCE -- AN IN-DEPTH ANALYSIS BASED ON THE CHANGE OF EMOTIONAL BEHAVIOR AND COGNITIVE PSYCHOLOGY

아카디아 프랑스어에 대한 사회언어학적 연구 : ‘시아크(chiac)’를 중심으로

This paper is a first attempt to understand “Chiac” and its goal is to determine the degree of linguistic interference between French and English in a language contact situation where French is the minority language. To this end, we analyzed about 5,000 dialogues in France Daigle’s novel <Pour sûr> and revealed the following two facts through a series of discussions centered on “borrowing scales” and “integration.” First, through corpus analysis, we confirmed that the lexical categories most sensitive to borrowing are nouns, followed by verbs, adjectives, and adverbs in the order of how greatly they are influenced by English. Conjunctions, interjections, prepositions, and pronouns were also borrowed from English but the frequency of their borrowing was less high than those of the higher-ranking lexical categories. Second, in the case of adjectives, conjunctions, and verbs, even if they were borrowed from English, they were often assimilated into the unique grammatical rules of French, which differed from English. In the case of adjectives and verbs, inflections according to gender, number, and derivation according to person appeared; in the case of conjunctions, there was no morphological change but they did not have the power to ruin the grammatical structure of French. From the above two facts, we found that in the case of Chiac, factors such as function of words in a sentence, morphosyntactic differences, and complexity of grammar can affect the degree of language interference. This study hopes to resolve the serious imbalance in domestic studies and promote a broader understanding of French in North America.

Assessing Corpus Evidence for Formal and Psycholinguistic Constraints on Nonprojectivity

Abstract Formal constraints on crossing dependencies have played a large role in research on the formal complexity of natural language grammars and parsing. Here we ask whether the apparent evidence for constraints on crossing dependencies in treebanks might arise because of independent constraints on trees, such as low arity and dependency length minimization. We address this question using two sets of experiments. In Experiment 1, we compare the distribution of formal properties of crossing dependencies, such as gap degree, between real trees and baseline trees matched for rate of crossing dependencies and various other properties. In Experiment 2, we model whether two dependencies cross, given certain psycholinguistic properties of the dependencies. We find surprisingly weak evidence for constraints originating from the mild context-sensitivity literature (gap degree and well-nestedness) beyond what can be explained by constraints on rate of crossing dependencies, topological properties of the trees, and dependency length. However, measures that have emerged from the parsing literature (e.g., edge degree, end-point crossings, and heads’ depth difference) differ strongly between real and random trees. Modeling results show that cognitive metrics relating to information locality and working-memory limitations affect whether two dependencies cross or not, but they do not fully explain the distribution of crossing dependencies in natural languages. Together these results suggest that crossing constraints are better characterized by processing pressures than by mildly context-sensitive constraints.

The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions

Not all misinformation is created equal. It can adopt many different forms like conspiracy theories, fake news, junk science, or rumors among others. However, most of the existing research does not account for these differences. This paper explores the characteristics of misinformation content compared to factual news—the “fingerprints of misinformation”—using 92,112 news articles classified into several categories: clickbait, conspiracy theories, fake news, hate speech, junk science, and rumors. These misinformation categories are compared with factual news measuring the cognitive effort needed to process the content (grammar and lexical complexity) and its emotional evocation (sentiment analysis and appeal to morality). The results show that misinformation, on average, is easier to process in terms of cognitive effort (3% easier to read and 15% less lexically diverse) and more emotional (10 times more relying on negative sentiment and 37% more appealing to morality). This paper is a call for more fine-grained research since these results indicate that we should not treat all misinformation equally since there are significant differences among misinformation categories that are not considered in previous studies.

A Chinese Grammatical Error Correction Method Based on Iterative Training and Sequence Tagging

Chinese grammatical error correction (GEC) is under continuous development and improvement, and this is a challenging task in the field of natural language processing due to the high complexity and flexibility of Chinese grammar. Nowadays, the iterative sequence tagging approach is widely applied to Chinese GEC tasks because it has a faster inference speed than sequence generation approaches. However, the training phase of the iterative sequence tagging approach uses sentences for only one round, while the inference phase is an iterative process. This makes the model focus only on the current sentence’s current error correction results rather than considering the results after multiple rounds of correction. In order to address this problem of mismatch between the training and inference processes, we propose a Chinese GEC method based on iterative training and sequence tagging (CGEC-IT). First, in the iterative training phase, we dynamically generate the target tags for each round by using the final target sentences and the input sentences of the current round. The final loss is the average of each round’s loss. Next, by adding conditional random fields for sequence labeling, we ensure that the model pays more attention to the overall labeling results. In addition, we use the focal loss to solve the problem of category imbalance caused by the fact that most words in text error correction do not need error correction. Furthermore, the experiments on NLPCC 2018 Task 2 show that our method outperforms prior work by up to 2% on the F0.5 score, which verifies the efficiency of iterative training on the Chinese GEC model.

Construction of Online English Offline Teaching Mode in the Era of Internet Plus

With the rapid development of Internet technology, its application in the field of education is more extensive and diversified, and plays a greater role in teaching. Grammar teaching is an important part of English teaching. The complexity of grammar in forestry English leads to the common problems of English grammar among students. Relying on the Internet technology to establish the cloud classroom intelligent platform, online and offline interactive English teaching can fully play the role of online teaching resources and information technology by combining online and offline. This method can activate the classroom and improve students' language application ability and learning interest. Based on this, this paper analyzes the application of cloud classroom intelligent platform in English teaching. Then this paper discusses its construction method and application strategy, aiming to provide reference for relevant researchers.

STUDENTS’ DIFFICULTIES IN READING COMPREHENSION AT THE EIGHT GRADE STUDENTS OF SMPN 22 KOTA JAMBI

This research aimed to analyze students' difficulties in reading comprehension at SMPN 22 Jambi. This is a qualitative research. The technique of data collection used in this research is observation and interviews. The subjects in this study were students of class VIII G at SMPN 22 Jambi. The results of this study indicate that the difficulties faced by class VIII G students in reading comprehension are four difficulties. Firstly, students have difficulty in understanding complexity of grammar. Secondly, the students has difficulty in knowing certain vocabulary when understanding reading. Thirdly, students have difficulty in using reading strategies, and the last one the students have difficulty in concentrations.

The Level of Accuracy and Grammatical Complexity in Written Texts by Chinese Students

The influence of the first language (L1) on the acquisition of a second language (L2) is inevitable. In addition, when L1 and L2 do not belong to the same language family, a negative influence, i.e., an interference, will occur. The current study aims to investigate the level of accuracy and grammar complexity in texts written by Chinese upper secondary school students from the perspective of language transfer. It involves an analysis of a small corpus comprising 54 texts with the use of the terminable unit (T-unit) measure. The finding shows that the Chinese writers produced a text with only a few error-free T-units largely due to the syntactical transfer from Chinese to English. With regard to grammatical complexity, subordinate clauses are frequent in the corpus but relative clauses are rare.

An Entropy Metric for Regular Grammar Classification and Learning with Recurrent Neural Networks.

Recently, there has been a resurgence of formal language theory in deep learning research. However, most research focused on the more practical problems of attempting to represent symbolic knowledge by machine learning. In contrast, there has been limited research on exploring the fundamental connection between them. To obtain a better understanding of the internal structures of regular grammars and their corresponding complexity, we focus on categorizing regular grammars by using both theoretical analysis and empirical evidence. Specifically, motivated by the concentric ring representation, we relaxed the original order information and introduced an entropy metric for describing the complexity of different regular grammars. Based on the entropy metric, we categorized regular grammars into three disjoint subclasses: the polynomial, exponential and proportional classes. In addition, several classification theorems are provided for different representations of regular grammars. Our analysis was validated by examining the process of learning grammars with multiple recurrent neural networks. Our results show that as expected more complex grammars are generally more difficult to learn.

Intelligence Perception from Text: Accuracy and Cues

The present study deals with the first impression about a person’s intelligence, based solely on their text. We attempted to calculate the accuracy of a reader’s judgment, and to determine which features of the text are associated with both the reader’s impression as well as the author’s intelligence test scores (Brunswik lens model). The study involved 116 participants aged 16 to 23 years. Each of them read and evaluated 4 out of 20 texts that were written by other participants in an earlier study. Participants evaluated both the general impression of the author’s intelligence and of the text itself, scoring responses to questions such as “How smart is the author of this text?” and to other text features that may be associated with intelligence judgement, including “vocabulary richness” and “grammar complexity”. In addition, objective indicators such as readability (Flesch-Kincaid score), length, and relative errors were calculated for the texts. The accuracy of reader judgments, estimated in a text-based analysis as the Spearman correlation coefficient between the “how smart is the author” score and the actual author test scores, did not reach statistical significance. However, the data indicate the presence of a nonlinear relationship. As revealed by a participant-based analysis, authors from the lower quartile (based on test scores) tend to be rated lower by participants as well. Readability, vocabulary richness, grammar complexity, and the relative number of words longer than six letters all were utilized by the reader participants in their judgments (i.e., correlated with impression scores) and at the same time were valid (i.e., correlated with test scores). We discuss the problem of the narrowness of scientific and psychological impressions on intelligence, compared to everyday perceptions

The contribution of An Interactive Multimedia in Teaching Simple Present Tense for Junior High Student

Learning English in Junior High School emphasizes on four language skills, they are speaking,writing, reading,and listening. However, it sometimes collides with the complexity of grammar, one of which is learning the present simple present tense in which for some people is acomplicated matter and makes it difficult for them in understanding English. Departing from this problem, a new breakthrough is needed, so that students are interested in learning English. Today’s education emphasizes on how to learn by utilizing technological sophistication, then Multimedia is one of the technologies that can be used to stimulate thoughts, feelings, concerns, and abilities or skills of learners. This technology has a very positive impact on students since it can provide solutions and ease in carrying out the learning process. The purpose of the study was to describe the implementation of interactive multimedia and to describe the level of defiance of the eighth grade students of SMP Muhammadiyah I Surakarta in learning of Simple Present Tense. The study employed a quantitative research approach, and the type used was an experiment. The experimental design in this study was randomized pre-test post-test design. The sample in this study was class VIII A, assigned as the Experiment Group 1 (EG1) who was taught by using interactive multimedia, while class VIII B, assigned as the Experimental Group 2 (EG2) who was taught by using the lecturing method and supported by using a handout. After learning was complete, both groups were given the same post-test. The result found that the EG1 who given treatment by using interactive multimedia got higher results than the EG2 who treated by Handout, where the mean score of the EG1 was 83.04, while the mean score of the EG2 was 72.96. Based on the result above, it was indicated that the students of SMP Muhammadiyah I Surakarta was more comprehend in learning The Simple Present Tense by using interactive multimedia better than conventionally, since interactive multimeda can build their creativity power and increase their understanding in English.

Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning

In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language processing, have witnessed remarkable improvements in state of the art models. One advantage of this technological boost is to facilitate comparison between different neural networks and human performance, in order to deepen our understanding of human cognition. Here, we investigate which neural network architecture (feedforward vs. recurrent) matches human behavior in artificial grammar learning, a crucial aspect of language acquisition. Prior experimental studies proved that artificial grammars can be learnt by human subjects after little exposure and often without explicit knowledge of the underlying rules. We tested four grammars with different complexity levels both in humans and in feedforward and recurrent networks. Our results show that both architectures can “learn” (via error back-propagation) the grammars after the same number of training sequences as humans do, but recurrent networks perform closer to humans than feedforward ones, irrespective of the grammar complexity level. Moreover, similar to visual processing, in which feedforward and recurrent architectures have been related to unconscious and conscious processes, the difference in performance between architectures over ten regular grammars shows that simpler and more explicit grammars are better learnt by recurrent architectures, supporting the hypothesis that explicit learning is best modeled by recurrent networks, whereas feedforward networks supposedly capture the dynamics involved in implicit learning.