Language Sentences Research Articles

Benefactive declarative sentences in Indonesian: The role of the beneficiary in social behavior representation

Language shows a nation's character. Can Indonesian sentences with the predicate benefactive verbs show the character of the Indonesian nation? Benefactive sentences are sentences that have a subject argument as the beneficiaries and an object argument as the agent or vice versa. Subjects as agents and objects as beneficiaries. Beneficiaries can be oneself and can also be other people or personal things. In the Indonesian language by the beneficial declarative sentences, it can be determined the dominance of the beneficial. The problem is, what is the impact of the dominance of the beneficiary role on the behavior of Indonesian language users and what is the agent's relationship with our social beneficiaries and its social impact? The purpose of this study is to reveal the impact of dominance of the role of beneficiary in the benefactive verb on the declarative sentence of the Indonesian language, and the relationship of the agent with the beneficiary of circumstances or other people and their social impact. This research is qualitative research. The research data is in the form of declarative sentences in Indonesian which are predicated of benefactive verbs in people's speech, elementary-high school Indonesian language books, middle school economics books, and research data exploration. Research data was collected using observation and documentation methods using note-taking techniques and analyzed using distributional methods, interpretation, and analysis of meaning components. The results of this research can be concluded that beneficial declarative sentences in Indonesian show the behavior of the Indonesian people which is selfish, liberalist, and hedonistic, not socialist. The implications of these characteristics are for the nation's behavior, including corrupt public officials (only concerned with personal, family, and group profits)

Syntax and Morphology Interface: A Study within Lexical- Functional Grammar

The present paper is aimed at providing information on the interaction of syntax and morphology within Lexical-Functional Grammar (hereinafter LFG). It is to introduce how c-structure and f-structure as levels of representation in LFG are assigned to sentences in English Language. This paper is also an investigation of how distinct syntactic relations are obtained when changing words or phrases in sentence structures. This is obviously shown in c-structure and f-structure. The former denotes the constituent structure of sentences which entails the composition of words into phrasal constituents hierarchically. Whereas, the latter represents the grammatical relations between the units of a sentence, which includes subject, object, complement, adverbial, and so forth. Further, the aim of the paper is to cast light on different important phenomena that are the principal concern of LFG viz head mobility, passive, and wh-movement. Finally, how changes of lexical forms of processes and participants assign verbs with different functions and changes the structure of sentences.

Comparison of Four Types of Interrogative Sentences in Chinese and Arabic Language (A Grammatical Study) Grammar

An interrogative sentence is a common sentence used in daily life and communication. The main purpose of an interrogative sentence is to ask questions that the inquirer does not yet know and expects to be answered. There are many types of interrogative sentences in Chinese, mainly categorized into four types, namely general interrogative sentences, special interrogative sentences, selective interrogative sentences, and repeated interrogative sentences. While each type of interrogative sentence has its unique grammatical form and way of answering, there exist similarities among them. Question marks should be added at the end of interrogative sentences, and the tone at the end of the sentence should also go high in pitch. The topic of Arabic and Chinese interrogative sentences has been discussed and studied by many linguistic experts and researchers with many rare research results achieved. Based on previous articles on interrogative sentences, as well as the researchers’ in-depth study of Arabic and Chinese interrogative sentences, but there are very few comparative studies on Chinese and Arabic interrogative sentences. Therefore, to analyze and compare interrogative sentences in these two different languages more comprehensively, researchers will summarize, compare and analyze interrogative sentences in Chinese and Arabic. This paper first adopts a descriptive method to collect information and articles about Chinese and Arabic interrogative sentences, thus laying the quantitative foundation for the research content. The information collected in this article is mainly from grammar textbooks and books written by prominent linguists in Chinese and Arabic, journals published by linguists and researchers, and doctoral and master's theses. The paper then studies various types of interrogative sentences in Chinese and Arabic as the focus, with special attention paid to the similarities and differences presented by the two languages. Keywords: Arabic interrogative sentence, Chinese interrogative sentence, interrogative tool, comparative interrogative sentence, question type.

INTERFERENCE IN LEARNING ENGLISH LANGUAGE: AN ANALYSIS OF SENTENCE CONSTRUCTION IN THE WRITTEN WORK OF YEAR 3 ESL LEARNERS IN WANGSA MAJU PRIMARY SCHOOL, MALAYSIA

ABSTRACT One of the most significant and interesting aspects of human development is language acquisition. Since English is a universal language, it is an essential language of interaction and education. Therefore, we have to take this language of education for professional competence seriously. The aim of the study is to explore the analysis of cultural interference, an aspect that significantly hinders the learning and acquisition of English among young Malay learners. It aims to identify the students’ errors in sentence construction, particularly on the occurrence of mistakes in subject-verb-agreement’ (SVA), determiners, and the copula ‘be’. The data of this study came from an exploratory study of errors in the written essays by Year 3 students in Wangsa Maju Primary School, Malaysia. The findings of the study showed that the students were struggling and having difficulty in using accurate English grammar in their academic writing. The results revealed that the most frequent errors were the incorrect usage of the copula ‘be’ and the absence of determiners. The interference of mother tongue played a vital cause towards inhibiting the production of error free English language sentences. Lack of understanding of how the English language grammar function also accounted to this. All these could be seen as interlingual errors whereby the native language of the participants influenced their writing patterns.

Особенности выражения категории фокуса в расщепленном предложении английского языка

<p>The paper addresses the interaction of the focus category and the semantic and syntactic aspects of the cleft sentence of the English language. The features of the focus in a cleft sentence are discussed, taking into account the relationship between the information structure and the category of definiteness-indefiniteness. The authors present a review of studies that investigate the information structure of it-clefts in terms of generative grammar. Two groups of studies are distinguished: the one of the groups integrates the focus into the deep structure of it-clefts; the other group of studies attributes the focus to the surface structure of it-clefts. It is concluded that the study of the role of the focus of clefts in isolation from the discourse leads to the interpretation of the focus exclusively in terms of semantic and structural relations. Consequently, the transformational rules formulated in the studies reviewed may not be universal and differently describe the process of transition to the surface structure of a cleft sentence in different languages.</p>

CNL2ASP: Converting Controlled Natural Language Sentences into ASP

Abstract Answer set programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Although ASP has gained widespread acceptance in academic and industrial contexts, there are certain user groups who may find it more advantageous to employ a higher-level language that closely resembles natural language when specifying ASP programs. In this paper, we propose a novel tool, called CNL2ASP, for translating English sentences expressed in a controlled natural language (CNL) form into ASP. In particular, we first provide a definition of the type of sentences allowed by our CNL and their translation as ASP rules and then exemplify the usage of the CNL for the specification of both synthetic and real-world combinatorial problems. Finally, we report the results of an experimental analysis conducted on the real-world problems to compare the performance of automatically generated encodings with the ones written by ASP practitioners, showing that our tool can obtain satisfactory performance on these benchmarks.

Differential Analysis of Stylistic Features in Chinese-English Interpretation Based on Natural Language Processing

Abstract In this paper, after researching the natural language processing model, we use the knowledge representation technology to construct a knowledge base and use the sequence comparison algorithm to compare the sequences of Chinese and English translations, analyze the similarities and differences between the two sequences, and analyze the entropy of information between English and Chinese translations through the probabilistic theory and mathematical statistics method. On this basis, we analyze the differences between Chinese and English translations in terms of lexical dimension, syntactic dimension and articulation dimension and explore the differences in lexical richness and thematic concentration between Chinese and English based on “entropy”. The results show that Chinese is significantly higher than English translations in terms of language output length, sentence complexity, subordinate structure and phrase structure (p<0.001), and Chinese is significantly higher than English translations in terms of articulation (p<0.05). This paper provides an important reference value for studying the differences in stylistic features in Chinese-English translation.

Using sentence processing speed and automaticity to predict L2 performance in the productive and receptive tasks

Abstract We investigated the predictive capacity of second language (L2) sentence processing speed measured using reaction time (RT) and automaticity captured using the coefficient of variation (CV) on the accuracy of L2 use in receptive and productive tasks. A total of 74 Chinese English-L2 learners completed a maze task, an elicited imitation task (EIT), and a timed grammaticality judgment task (TGJT) using comparable sentences. RT and CV from the maze task were used to predict the use of L2 morphological markers in the EIT and TGJT. The findings revealed that there were no significant effects of RT and CV in the EIT. However, in the TGJT, the effect of CV was significant. These results highlight the predictive power of L2 processing automaticity in the receptive task involving L2 morphological use.

The structure correspondence hypothesis predicts how word and sentence in language correlate with term and principle in mathematics.

The association between language and mathematics is an important debated topic. Here, we proposed a structure correspondence hypothesis to explain under what conditions language and mathematics are closely related. According to the hypothesis, there would be an association when they have equivalent structure. One hundred and fifty high school students were recruited to finish mathematical and language tests at the element level (i.e., geometric term processing and word analogy) and at the low-dimensional combination level (i.e., geometric principle processing and sentence completion) as well as the tests to measure cognitive covariates (general intelligence and spatial processing). After controlling for age, gender and cognitive covariates, geometric term processing and word analogy were closely correlated, and geometric principle processing and sentence completion were significantly correlated. No other correlations were found. The results support the structure correspondence hypothesis and provide a new perspective of structure of language and verbalized mathematics for the relation between language and mathematics.

Familiarity with the structure of the sentence helps us to understand correctly

Sentence is the most important grammatical structure in many languages. If we want to know the meaning of a paragraph, or paragraphs, we should know the meaning of each sentence. Sentence specifies the appropriate meaning and role for every lingual element. If we disorganize the system of one sentence, we will have some words with no complete meaning; some of these words may have different meanings and some of them may take different roles like a noun, a verb, an adjective or an adverb. Suppose! If we disorganise the whole sentences of a paragraph or all paragraphs, it will be impossible to know the meaning of one paragraph or paragraph because the most significant structure of the language (sentence) is disorganized. In conclusion, sentences are very important for many languages. Basically, each book is made from sentences. If we have no sentence, we have no paragraph or paragraphs; it means the sentence is everything in many languages.

AI Content Generation Technology based on Open AI Language Model

The Open AI language model is a powerful tool for generating AI content. A Large amount of text data is trained through the language model, which can generate new text that is similar in style and tone to the training data. This language model can assist writers in generating high-quality content by offering suggestions and insights for improving language usage, sentence structure, and overall readability. This study represents the development of a content generation tool based on the open AI language model by utilising GPT 3 in the backend as an API to generate the necessary information for the model. With the help of this tool, businesses and individuals can produce high-quality, engaging content more efficiently than ever before. This content generation tool uses a recurrent neural network (RNN) architecture, which enables it to make more accurate predictions than rule-based chatbots. All the features, like Facebook ads, LinkedIn posts, Amazon product descriptions, blogs, company bios, chat bots, and so on, will be presented in the dashboard. This tool is powered by advanced machine learning algorithms that can analyse and understand natural language, allowing them to produce content that is grammatically correct, free of errors, and tailored to specific audiences. They can also help optimize content for search engines, ensuring that it reaches a wider audience and generates more traffic with fine-tuning templates.

Strahler number of natural language sentences in comparison with random trees

The Strahler number was originally proposed to characterize the complexity of river bifurcation and has found various applications. This article proposes a computation of the Strahler number’s upper and lower limits for natural language sentence tree structures. Through empirical measurements across grammatically annotated data, the Strahler number of natural language sentences is shown to be almost 3 or 4, similar to the case of river bifurcation as reported by Strahler (1957 Eos Trans. Am. Geophys. Union 38 913–20). Based on the theory behind this number, we show that there is a kind of lower limit on the amount of memory required to process sentences. We consider the Strahler number to provide reasoning that explains reports showing that the number of required memory areas to process sentences is 3–4 for parsing (Schuler et al 2010 Comput. Linguist. 36 1–30), and reports indicating a psychological ‘magical number’ of 3–5 (Cowan 2001 Behav. Brain Sci. 24 87–114). An analytical and empirical analysis shows that the Strahler number is not constant but grows logarithmically. Therefore, the Strahler number of sentences is derived from the range of sentence lengths. Furthermore, the Strahler number is not different for random trees, which could suggest that its origin is not specific to natural language.

Form of Magic Words in The Process of Language Acquisition for Children Aged 2-4 Years

This research aims to describe the early childhood language acquisition process at KB Aisyiyah 1 Balun, Cepu District, Blora Regency. This type of research is descriptive qualitative. Two research subjects were taken in each group, so there were six research subjects. The data collection method used several techniques, namely observation, listening, recording, and note-taking. The research results show that children can achieve language competence at the phonology, morphology, syntax, and semantics levels. Language acquisition results in phonology show that the phonemes /r//l/ sound faint. Phoneme decay is the melting of phonemes due to morphological processes. The /l/ phoneme decays when the/y/phoneme's sound changes to the /l/ phoneme. The results of language processing in the field of morphology are that children aged two years have just mastered the basic morphemes that are pronounced even though the pronunciation is not yet clear. Meanwhile, many sulfides (- kan ) are found in children aged four years. Early childhood language acquisition in the area of syntax identified phrase errors, children's composition of first language sentences was mixed up, and sentence structures were adequate, although sometimes grammatically, sometimes inverted. Meanwhile, language acquisition at the semantic level tends to use denotative Meaning in every utterance.

Features of Using Models Pspice Software

The paper notes the features of the input language of Pspice software package, methods of describing sentences of the input language, building own models of the studied object and using the models specified in the open library of this complex. Possible operating modes of the complex and methods of their effective use are described. The results obtained make it possible to carry out effective circuit and technical modeling and analysis of both known objects and newly created ones, while ensuring the economy of material and human resources

Audio Feature Space Analysis for Emotion Recognition from Spoken Sentences

An analysis of low-level feature space for emotion recognition from the speech is presented. The main goal was to determine how the statistical properties computed from contours of low-level features influence the emotion recognition from speech signals. We have conducted several experiments to reduce and tune our initial feature set and to configure the classification stage. In the process of analysis of the audio feature space, we have employed the univariate feature selection using the chi-squared test. Then, in the first stage of classification, a default set of parameters was selected for every classifier. For the classifier that obtained the best results with the default settings, the hyperparameter tuning using cross-validation was exploited. In the result, we compared the classification results for two different languages to find out the difference between emotional states expressed in spoken sentences. The results show that from an initial feature set containing 3198 attributes we have obtained the dimensionality reduction about 80% using feature selection algorithm. The most dominant attributes selected at this stage based on the mel and bark frequency scales filterbanks with its variability described mainly by variance, median absolute deviation and standard and average deviations. Finally, the classification accuracy using tuned SVM classifier was equal to 72.5% and 88.27% for emotional spoken sentences in Polish and German languages, respectively.

PRINCIPLES OF DIVISION OF SIMPLE SENTENCE TYPES IN ANCIENT TURKIC LANGUAGE

In this article, the types of sentences in the ancient Turkic language according to their structure and the peculiarities of simple sentences are analyzed. The types of simple sentences in the ancient Turkic language and the views of representatives of linguistics on the division of simple sentences into types are comparatively studied.

Energy-based features and bi-LSTM neural network for EEG-based music and voice classification

AbstractThe human brain receives stimuli in multiple ways; among them, audio constitutes an important source of relevant stimuli for the brain regarding communication, amusement, warning, etc. In this context, the aim of this manuscript is to advance in the classification of brain responses to music of diverse genres and to sounds of different nature: speech and music. For this purpose, two different experiments have been designed to acquire EEG signals from subjects listening to songs of different musical genres and sentences in various languages. With this, a novel scheme is proposed to characterize brain signals for their classification; this scheme is based on the construction of a feature matrix built on relations between energy measured at the different EEG channels and the usage of a bi-LSTM neural network. With the data obtained, evaluations regarding EEG-based classification between speech and music, different musical genres, and whether the subject likes the song listened to or not are carried out. The experiments unveil satisfactory performance to the proposed scheme. The results obtained for binary audio type classification attain 98.66% of success. In multi-class classification between 4 musical genres, the accuracy attained is 61.59%, and results for binary classification of musical taste rise to 96.96%.

Speech data collection system for KUI, a Low resourced tribal language

<p>A new generation of speech translation technology is being developed to enable natural cross-language communication. Research efforts must focus on large vocabulary, spontaneous speech, and speaker variances to accommodate the varying demands of speech recognition technologies. These are important issues that need to be resolved for the general application of voice recognition in realistic settings. Most languages with limited resources don’t even have any speech data. Creating speech corpora is extremely difficult and time-consuming. Among all, KUI is regarded as one of the low-resource languages. In this paper, we developed the speech dataset for the KUI language to document and preserve their culture, tradition, and history for future generations. We also discuss the design, data collection procedures, and implementations and outline the different research possibilities using our KUI dataset. This paper mainly describes the GUI and method for the collection of KUI speech more quickly. In this section, the statistics of the people who helped and contributed to the collection of this KUI dataset have been provided. This study details a novel method of gathering data for any speech dataset. Using this process, we collected 60 hours of speech data sampled at 16 kHz by three different devices such as a Zoom recorder, Mobile, and Laptop from 80 different speakers. Each speaker contributed 500 sentences in the KUI language. A GUI application is designed to capture the speeches of numerous speakers in the KUI language. Several guidelines are proposed and used for the collection of the KUI speech dataset. All the guidelines are based on real-time experience gained during the data collection process by our team members.</p>

Can language models be used for real-world urban-delivery route optimization?

Language models have contributed to breakthroughs in interdisciplinary research, such as protein design and molecular dynamics understanding. In this study, we reveal that beyond language, representations of other entities, such as human behaviors, that are mappable to learnable sequences can be learned by language models. One compelling example is the real-world delivery route optimization problem. We here propose a novel approach based on the language model to optimize delivery routes on the basis of drivers' historical experiences. Although a broad range of optimization-based approaches have been designed to optimize delivery routes, they do not capture the implicit knowledge of complex delivery operating environments. The model we propose integrates this knowledge in the route optimization process by learning from driving behaviors in experienced drivers. A real-world delivery route that preserves drivers' implicit behavioral patterns is first analogized to a sentence in natural language. Through unsupervised learning, we then learn the vector representations of words and infer the drivers' delivery chains on the basis of the tailored chain-reaction-based algorithm. We also provide insights into the fusion of language models and operations research methods. In our approach, language models are applied to learn drivers' delivery behaviors and infer new deliveries at the delivery zone level, while the classic traveling salesman problem (TSP) model is embedded into the hybrid framework for intra-zone optimization. Numerical experiments performed on real-world data from Amazon's delivery service demonstrate that the proposed approach outperforms pure optimization, supporting the effectiveness, efficiency, and extensibility of our model. As a versatile approach, the proposed framework can easily be extended to various disciplines in which the data follow certain grammar rules. We anticipate that our work will serve as a stepping stone toward the understanding and application of language models in tackling interdisciplinary research problems.

Explainable Meta-Path Based Recommender Systems

Meta-paths have been popularly used to provide explainability in recommendations. Although long/complicated meta-paths could represent complex user-item connectivity, they are not easy to interpret. This work tackles this problem by introducing a meta-path translation task. The objective is to translate a meta-path to its comparable explainable meta-paths that perform similarly in terms of recommendation but have higher explainability compared to the given one. We propose a definition of meta-path explainability to determine comparable explainable meta-paths and a meta-path grammar that allows comparable explainable meta-paths to be formed in a similar way as sentences in human languages. Based on this grammar, we propose a meta-path translation model, a sequence-to-sequence (Seq2Seq) model to translate a long and complicated meta-path to its comparable explainable meta-paths. Two novel datasets for meta-path translation were generated based on two real-world recommendation datasets. The experiments were conducted on these generated datasets. The results show that our model outperformed state-of-the-art Seq2Seq baselines regarding meta-path translation and maintained a better trade-off between accuracy and diversity/readability in predicting comparable explainable meta-paths. These results indicate that our model can effectively generate a group of explainable meta-paths as alternative explanations for those recommendations based on any given long/complicated meta-path.