Grammar Model Research Articles

Cultivation of Construction Model of Translation Grammar from the Perspective of Cognitive Grammar

Cultivation of Construction Model of Translation Grammar from the Perspective of Cognitive Grammar

Research and Implementation of English Grammar Check and Error Correction Based on Deep Learning

English as a universal language in the world will get more and more attention, but English is not our mother tongue, and there exist differences in culture and thinking. English grammar is the most difficult problem to solve. There are many English learners, and the number of English teachers is limited, and it is inevitable to use Internet technology to solve the problem of lack of resources. The article uses deep learning technology to propose an ASS grammar detection model, which can quickly and efficiently detect grammatical errors. The research results show the following. (1) This study selects data from the GEC evaluation task and analyzes the four modules of article, noun, verb, and preposition through algorithms under different models. The results indicate the accuracy of the four modules. The recall rate has been improved to a certain extent, the accuracy rate of nouns is the highest, which can reach 63.99%, the accuracy rate of prepositions is improved to a lesser extent, and the inspection accuracy rate after improvement is 12.79%. (2) In the experiment to verify the effectiveness of the ASS grammar detection model, compared with the detection effect of the ordinary model, the accuracy of the ASS comprehensive inspection has been greatly improved. The comprehensive accuracy of the ordinary detection model is 28.01%, and the ASS model’s comprehensive accuracy rate of the inspection was 82.82%, and the accuracy rate was increased by 54.81%. The result shows that the performance of the ASS inspection model has been improved by leaps and bounds compared with the traditional model. (3) After transforming and upgrading the ASS model, the three models and other models obtained were run on the test set and the mixed test set, respectively. The results show that the accuracy, precision, recall, and F1 score of ASS model are the highest in the test set, which are 98.71%, 98.83%, 98.64%, and 98.73%, respectively, the Bayesian network check model has the lowest accuracy rate of 51.74%, and the ROC curve value and AUC value of the ASS model are both the largest. The accuracy of the ASS model on the mixed test set is also the highest, reaching 98.01%. The JaSt model on the mixed test set has a significant downward trend, with the accuracy rate dropping from 92.16% to 56.68%. It can be concluded that the ASS model can accurately and efficiently monitor grammatical errors.

A self-organized sentence processing theory of gradience: The case of islands

Formal theories of grammar and traditional parsing models, insofar as they presuppose a categorical notion of grammar, face the challenge of accounting for gradient effects (sentences receive gradient acceptability judgments, speakers report a gradient ability to comprehend sentences that deviate from idealized grammatical forms, and various online sentence processing measures yield gradient effects). This challenge is traditionally met by explaining gradient effects in terms of extra-grammatical factors, positing a purely categorical core for the language system. We present a new way of accounting for gradience in a self-organized sentence processing (SOSP) model. SOSP generates structures with a continuous range of grammaticality values by assuming a flexible structure-formation system in which the parses are formed even under sub-optimal circumstances by coercing elements to play roles that do not optimally suit them. We focus on islands, a family of syntactic domains out of which movement is generally prohibited. Islands are interesting because, although many linguistic theories treat them as fully ungrammatical and uninterpretable, experimental studies have revealed gradient patterns of acceptability and evidence for their interpretability. We describe the conceptual framework of SOSP, showing that it largely respects island constraints, but in certain cases, consistent with empirical data, coerces elements that block dependencies into elements that allow them.

Dynamic Modeling and Analysis of Micro-Video Teaching System Based on Cloud Computing in College English Teaching

The era is developing and the society is improving. The application research of computer technology in the field of education is a hot topic at present. The cultivation of talents required the training of schools and teachers. How to cultivate talented person who meets the needs of society lied in the comprehensive ability of teaching in colleges and universities, so the college English education professional grammar teaching model is studied based on dynamic programming algorithm. After a brief overview of the dynamic rule algorithm, an algorithm for evaluating English grammar learning in colleges and universities is designed by using dynamic algorithm. The algorithm is composed of dynamic rule algorithm, language processing technology, and regular expression. In the subsequent experiments, it is proved that the algorithm has a good evaluation effect and can meet the actual application requirements.

Design of Chinese Grammar Recognition and Error Correction Model Based on the Deep Neural Network.

In order to further improve the performance of the automatic grammar error detection system, a new Chinese grammar recognition and correction model is proposed in this paper. Based on the transformer attention mechanism, the bias matrix of Gaussian distribution is added to improve the attention of the model to local text and strengthen the information extraction of wrong words and surrounding words in the wrong text. In addition, the ON_LSTM model is used to extract grammatical information from the special grammatical structure of error text. The experimental results show that the two methods can effectively improve the accuracy and recall rate, and the fused model achieves the highest F1 value. Finally, the Chinese text error correction system is designed to expand the application scope of the model, which helps to reduce the human cost in language learning.

ГРАМАТИЧНІ ФОРМИ СУБ’ЄКТАВ СЕМАНТИКО-СИНТАКСИЧНІЙСТРУКТУРІ РЕЧЕННЯ

The article is devoted to the analysis of case forms that implement the subjective meaning in the semantic structure of a simple sentence. The attention is paid to the noun forms of the nominative, vocative, genitive, dative, accusative and instrumental cases of an agent in the Ukrainian sentence. The grammar functions of an agent in different cases are considered, i.e. of a subject in the nominative and vocative cases, of an object in the genitive, dative, accusative and instrumental cases. Background. The analysis of the sentence semantics involves the study of the interaction between its semantic and formal layout. The interrelation between sentence semantic and syntactic structures reveals formal models of predicative grammar constructions. Purpose. The analysed models with subject syntaxemes denoting an agent, an experiencer, an actor, quantity, quality, and a location enable linguists and language learners to determine the grammatical means of expressing substantive (subject) semantics. Methods. In the study of grammatical forms of subject syntaxemes, the structural method is used, in particular, the methods of sentence model transformation and component analysis. In a number of cases, these techniques helped to reveal that a substantive syntaxeme with subject semantics, expressed in the indirect (genitive, dative, accusative, instrumental) case, is regularly transformed into a subject syntaxeme in the nominative case in the grammatical position of the subject. Results. The grammar models considered in the article demonstrate that the semantic subject of the Ukrainian sentence can be expressed by all case forms without preposition. The variety of forms and means of subject expression allows the noun and its equivalents to realize their agent function in a semantic-syntactic combination with predicates of both verbal and its derivative forms (noun, numeral, adjective, adverbial) nature. Conclusion. The main grammar form of expressing subjectivity is the nominative case combined with a predicate of action, state, process, quantitative, qualitative or locative feature. The vocative case regularly denotes the subject of addressed speech, the other morphological forms of subjects (genitive, dative, accusative, instrumental) have a much narrower application in the Ukrainian sentence.

Language Growth in Children with Mild to Severe Hearing Loss who Received Early Intervention by 3 Months or 6 Months of Age.

To evaluate the impact of hearing screening, diagnosis, and early intervention (EI) by 3 months or 6 months of age on language growth trajectories for children with hearing loss (HL) relative to children with normal hearing (NH). We recruited 133 children with mild to severe HL through universal newborn hearing screening records and referrals from audiologists in the United States and 116 children with NH who served as a comparison group. Examiners administered a battery of developmentally appropriate language measures between 12 months and 8 years of age. We constructed latent growth curve models of global language, grammar, and vocabulary using Bayesian statistics. Children with HL demonstrated no significant differences in initial language skills compared to children with NH. Children in the 1-3-6 group also showed no difference in language growth compared to children with NH. The slope for the 1-2-3 group was significantly steeper than children with NH for global language and grammar. This study documents the positive impact of EI on language outcomes in children with congenital HL. It is among the first to provide evidence to support the potential effects of very early intervention by 3 months of age.

Developing an English Grammar Practice Book based on Multicultural Values for Second-year University Students

The aim of this study is to develop an English grammar practice book model based on multicultural values as teaching materials for compulsory subjects of the university in Indonesia. This research-based on Research and Development approach, which is adopted from Gall, Gall, and Borg (2003). The data were collected through interviews and questionnaires. While qualitative data were analyzed using an interactive analysis and quantitative data analysis used percentages. The research participants consisted of lecturers and students at two universities in Indonesia. The findings showed that 1) the conceptual model of the English grammar practice book is applicable and contextual which presents the latest issues in accordance with the issue of Indonesian multicultural values. 2) The content of an English grammar practice book constitutes the following components: chapter titles, introductions, material presentation and elaboration of multicultural values, competence performances, summaries, reflections, variation exercise. It is suggested that the textbook model more help in developing multicultural competencies students in communication with other people from different backgrounds through using English as an international language.

Machine Learning-Based Grammar Error Detection Method in English Composition

The detection of grammatical errors in English composition is an important task in the field of NLP. The main purpose of this task is to check out grammatical errors in English sentences and correct them. Grammatical error detection and correction are important applications in the automatic proofreading of English texts and in the field of English learning aids. With the increasing influence of English on a global scale, a huge breakthrough has been made in the task of detecting English grammatical errors. Based on machine learning, this paper designs a new method for detecting grammatical errors in English composition. First, this paper implements a grammatical error detection model based on Seq2Seq. Second, this paper implements a grammatical error detection and correction scheme based on the Transformer model. The Transformer model performs better than most grammar models. Third, this paper realizes the application of the BERT model in grammar error detection and error correction tasks, and the generalization ability of the model has been significantly enhanced. This solves the problem that the forward and backward cannot be merged when the Transformer trains the language model. Fourth, this paper proposes a method of grammatical error detection and correction in English composition based on a hybrid model. According to specific application scenarios, the corresponding neural network model is used for grammatical error correction. Combine the Seq2Seq structure to encode the input sequence and automate feature engineering. Through the combination of traditional model and deep model, the advantages are complemented to realize grammatical error detection and automatic correction.

A new perspective on referentiality in elicited narratives: Introduction to the Special Issue

This special issue investigates the use of referential expressions in elicited picture-based narratives by children with and without developmental language disorders, across a range of languages and language combinations. All contributions use the Multilingual Assessment Instrument for Narratives (MAIN, Gagarina et al. 2012, 2019). The studies featured in this issue cover monolingual and bilingual children aged 4–11 years, but focus mainly on age 4–7, a period in a child’s life where great strides are made in the development of narrative skills. This collection of papers offers a new perspective on referentiality for several reasons: all studies use the same stimuli and by and large the same procedure for the elicitation of narratives. The stimuli, four picture-based stories, are controlled for comparability of protagonists, plot and story structure. They were designed as a ‘visual’ representation of a multidimensional model of story grammar. This methodological and theoretical base allows for a comparative investigation of referentiality (including reference introduction, maintenance and reintroduction) in narratives, across languages and populations. This introduction addresses theoretical aspects of referentiality in decontextualised discourse and reviews the literature regarding the impact of language-specific referential systems and the age and path of acquisition in typically developing children and children with developmental language disorders. We also discuss methodological aspects of eliciting referentiality in narratives in detail. This introduction thus seeks explanations for the diverse and sometimes contradictory empirical results regarding children’s mastery of referentiality. Finally, an overview of the contributions in the special issue is given.

Mixed computation

AbstractProof-theoretic models of grammar are based on the view that an explicit characterization of a language comes in the form of the recursive enumeration of strings in that language. That recursive enumeration is carried out by a procedure which strongly generates a set of structural descriptions Σ and weakly generates a set of strings S; a grammar is thus a function that pairs an element of Σ with elements of S. Structural descriptions are obtained by means of Context-Free phrase structure rules or via recursive combinatorics and structure is assumed to beuniform: binary branching trees all the way down. In this work we will analyse natural language constructions for which such a rigid conception of phrase structure is descriptively inadequate and propose a solution for the problem of phrase structure grammars assigning too much or too little structure to natural language strings: we propose that the grammar can oscillate between levels of computational complexity in local domains, which correspond to elementary trees in a lexicalised Tree Adjoining Grammar.

A Spatial Grammar Model for Designing Mass Customized High-rise Housing Blocks

Emergence as a product of natural processes is a typical feature for traditional and vernacular housing settlements created by different users with their various needs. However, this feature disappears in housing production approaches for uniform and standard users due to mass production concerns. The main reason is that user needs cannot be fully transferred to design strategies due to excessive standardization. One of the main factors in this is the need for design tools and methods that effectively evaluate preferences with complex relationships. Shape grammars and spatial grammars, one of the generative design approaches, exhibit original emergence examples with their rich diversity and adaptability to different approaches. Hence, it can be seen as an alternative for solving such problems. This article presents a parametric spatial grammar model that can design high rise housing blocks with customized dwelling for each family. The compositions of these houses ground on specific spatial relationships defined by parametric rule sets. In the article, the generative design process was developed in two stages. The first stage includes the formal, syntactical, and functional relations of the spaces in a floor plan, their representation according to the rules, and generating alternative solutions. The second stage consists of the representation of the facade rules and the parametric grammar that generates the facades. In the developed spatial grammar model, user preferences are transferred to the computer through the designed interface. Dimensional properties of housing blocks are defined by a gridal system, which is determined according to user preferences and used as a starting base for spatial grammar. Plan layouts are generated on this defined grid and by sequential application of rules. In conclusion, the adaptability of spatial grammar and the rich alternatives designers can use and develop are emphasized.

Research on attention and reasoning mechanism of autonomous English learning based on machine learning

The wave of the Internet era has swept over our lives and learning, and the Internet is gradually being widely used in all scenes of life and learning. The use of Internet technology can greatly improve efficiency compared to traditional methods. With the popularization of computers, internet technology has entered millions of households, and now more and more school courses are related to the Internet. However, the teaching evaluation of online learning is generally derived from the post-class feedback of teachers and students. In this paper, we analyze the video feeds of students during class and then qualitatively assess students' attention. In this paper, the English grammar error correction model is mainly based on deep learning, and the English grammar error correction problem is defined. The English grammar error correction evaluation criteria are given. Besides, the SEQ-based English grammar error correction model is proposed, and some improvements are made to the basic SEQ model, and then the algorithmic model is experimented and validated. Based on the above discussion, the feedback mechanism is proposed. Moreover, we also introduce the N-gram model and related implementation techniques, and experiments are conducted and experimental results are given with the corpus.

Realization and optimization of Chinese syntactic analyzer based on PCFG

As a basic research work, syntactic analysis is also a key technology in the field of natural language processing, and is the basis for deep understanding of language. The syntactic analyzer is an important tool for syntactic analysis. The Probabilistic Context Free Grammar (PCFG) model is the basic model of most parsers with excellent performance. Good effect. This article integrates named entity clues into PCFG-based Chinese syntax analysis models, completes joint learning and decoding of named entity recognition, syntactic analysis, and implements a fast and reliable decoding parser.

The Role of Metonymy in Teaching the Spanish Verbal System to L2/FL Learners of Spanish

This paper explores the application of conceptual metonymy (Lakoff, 1987; Ruiz de Mendoza, 2000; Langacker, 2009; Panther, Thornburg and Barcelona, 2009; Barcelona, 2013) in the development of pedagogical resources for the teaching of the Spanish verbal system to L2/FL learners. To this end, a description is given of the advantages of introducing inferential reasoning using metonymy based on certain principles taken from the Cognitive Grammar model (Langacker, 1987, 1991, 2000, 2001, 2008, 2009) in grammar teaching materials — both descriptions and activities. We focus on tense uses that involve metonymic processes, particularly, the meaning extensions in two Spanish past tenses: (1) the actional meaning of stative verbs when conveyed in pretérito indefinido (preterit), as in Pudimos comprar la casa 'We could buy the house', metonymically extended to Compramos la casa 'We bought the house'; and (2) the distancing use (uso citativo) of pretérito imperfecto (imperfect) when referring to current facts, as in ¿Cómo te llamabas? (How PRO.REFL.2SG call.IPFV.PST.2SG?) 'What was your name again?', which is metonymically extended to (Se me ha dicho/No recuerdo/No he oído) cómo te llamas [('I have been told/I can't recall/I couldn't hear') how PRO.REFL.2SG call.PRES.2SG]. In order to discuss the kind of contribution that this conceptual standpoint can make to the teaching of the Spanish verbal system, the pedagogical potential of some techniques and resources is explored in terms of (a) metonymic and metaphorical reasoning in pedagogical grammar descriptions, (b) consciousness-raising paraphrase exercises focused on meaning indeterminacy, and (c) network building and the use of (dynamic) images to show variable construals in grammatical meaning

The functions and evolution of xob ‘well’ in Persian: A thetical analysis

This study aims to explore the functions of the Persian xob ‘well’ and its evolution mechanisms. The Discourse Grammar model was mainly employed to analyze the occurrences of xob in the Bijankhān corpus. The data analysis revealed that xob is significantly frequent, especially in semi-formal and informal spoken genres. It also demonstrated that xob has risen from the adjective/adverb xub with several propositional meanings most of which converge on ‘good’. Having evolved from this meaning, the core procedural meaning of xob, i.e., ‘engaging in (positive) epistemic/affective consideration prefatory to continuation’, has developed metatextual functions in the three components of the situation of discourse. To express speaker's attitude and speaker-hearer interaction, xob mainly serves the functions of direct response (signaling acceptance, sufficiency, etc.), indirect response (signaling qualification, concession, etc.), and involvement prompting (signaling attention getting, backchannelling, and soliciting consensus). Meeting the needs of text organization, xob fulfills the dual purposes of framing the text pieces and transition between topics and events. The study also highlights the contribution of cooptation and late grammaticalization to the rise of the discourse markers xob and xeyli/besyār xob ‘very well’. Overall, these findings illustrate the potential of Discourse Grammar to address theticals such as discourse markers.

Towards a discourse-semantic approach to visual narrative analysis

Abstract The present study propounds a novel discourse-semantic approach that problematizes the social semiotic analysis of visual narrative in two respects: (i) the lack of a model that can explain the plurifunctional structure of visual acts of communication in general and (ii) the failure to provide the deep structure underlying the characters and/or objects in visual narrative in particular. Redressing these two shortcomings, the approach is methodologically geared towards analysing the visual narrative grammar that encodes the 2017 BBC image-enabled news story of Islamic State (IS). The proposed approach rests on two theoretical models: (i) Roman Jakobson’s (1960) communication model of language functions; (ii) Algirdas Julien Greimas’s (1966, 1987) structural-semantic model of actant grammar. The study has reached two major findings. First, theoretically, the visual narrative analysis of images demands the presence of both (1) a theory that can adequately explain the plurifunctional structure associated with the semiotic complexity of visual communication and (2) a structural-semantic model that reveals the deep structure of the actants that enable the dramatis personae to relate to the events featuring in the mono-/multimodal discourse of narrative. Second, on a practical level of the BBC’s visual storyline, IS has been represented within three actant-based enunciation-spectacles: (a) victimhood with Subject versus Object, (b) beneficiariness with Sender versus Receiver, and (c) villainy (self-presented and other-presented) with Opponent/Victim versus Helper.

Comparing Symbolic Models of Language via Bayesian Inference (Student Abstract)

Given recurring interest in structured representations in computational cognitive models, we extend a Bayesian scoring procedure for comparing symbolic models of language grammar. We conduct a case-study of modeling syntactic principles in German, providing preliminary results consistent with linguistic theory. We also note that dataset and part-of-speech (POS) tagger quality should not be taken for granted.

English Grammar Discrimination Training Network Model and Search Filtering

The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation model and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory.

Universal Grammar: Arguments for its Existence

The first part of this paper discusses the rationale for universal grammar (UG) theory to explain first language acquisition. It also illustrates the issues of language acquisition Chomsky argued which could not be supported by behaviourist theories and shows how Chomsky proposed a solution to this problem through his theoretical model of universal grammar. The next part outlines this theory’s key tenets, arguing that these principles must be an innate endowment of the human mind. Moreover, the study illustrates specific examples of grammatical phenomena that universal grammar seeks to explain. Lastly, it shows that certain distinct grammatical features are linked and that these connections can be explained within the Universal Grammar theoretical framework. The only reasonable explanation for the first language learning needs only limited linguistic exposure to activate them and set criteria for the language being learned for children whose minds have already been wired with essential language concepts.