Parsing Research Articles

The Passive Voice in Urdu: A Syntactic Investigation and Transformational Analysis

This paper explores passive structures in the Urdu language. It aims to understand their syntactic configuration and how to convert active sentences into passive ones. Limited research exists on Urdu's passivization compared to English. The research questions focus on the structure of passive sentences in Urdu and the necessary modifications for passivization. A qualitative research methodology is used, analyzing written texts and daily conversations of native Urdu speakers. The literature review compares Urdu's passive structures with English, including the standard "be" passive and less common "get passive" and "have passive." Findings show that Urdu passives use the auxiliary “Gaya” and the agent can be explicitly mentioned using "from-PP" rather than "by-PP." The paper also covers passivization in imperative sentences, using “ja:” and “cha:hi:e” morphemes. Moreover, voice neutrality is examined, showing that passive and active sentences in Urdu often convey the same event with similar agent-patient relationships. The study’s conclusive remarks on passive construction in Urdu have the potential to ignite future research in this domain. The paper contributes to the knowledge of passive constructions in Urdu which can significantly impact translation accuracy and interpretation between Urdu and English. This research also adds to the big picture of how languages work.

A syntactic evidence network model for fact verification

In natural language processing, fact verification is a very challenging task, which requires retrieving multiple evidence sentences from a reliable corpus to verify the authenticity of a claim. Although most of the current deep learning methods use the attention mechanism for fact verification, they have not considered imposing attentional constraints on important related words in the claim and evidence sentences, resulting in inaccurate attention for some irrelevant words. In this paper, we propose a syntactic evidence network (SENet) model which incorporates entity keywords, syntactic information and sentence attention for fact verification. The SENet model extracts entity keywords from claim and evidence sentences, and uses a pre-trained syntactic dependency parser to extract the corresponding syntactic sentence structures and incorporates the extracted syntactic information into the attention mechanism for language-driven word representation. In addition, the sentence attention mechanism is applied to obtain a richer semantic representation. We have conducted experiments on the FEVER and UKP Snopes datasets for performance evaluation. Our SENet model has achieved 78.69% in Label Accuracy and 75.63% in FEVER Score on the FEVER dataset. In addition, our SENet model also has achieved 65.0% in precision and 61.2% in macro F1 on the UKP Snopes dataset. The experimental results have shown that our proposed SENet model has outperformed the baseline models and achieved the state-of-the-art performance for fact verification.

Innovative Approach to Promoting Walkability in Lisbon

The increases in both the elderly population and urbanization present new problems for planning new cities and managing old cities. To solve this challenge, innovative ideas/proposals that encourage walkability and reduce fossil energy motor transport are needed, in line with the actions advocated by Sustainable Development Goal #11 (SDG11), Sustainable Cities and Communities. Opening passages that cross blocks, creating connections between the streets, is the proposal discussed in this paper, as seen in cities like Paris, Prague, Milan, and Juíz de Fora, which have passages in their urban fabric. These can be implemented in a building’s rehabilitation or when a new building replaces it. New architectural project designs should have several characteristics that promote walkability in the city, including passages and galleries. Although previous research has obtained promising results, extending the research to different urban fabrics and block sizes is needed. Using space syntax and data analysis, we simulated and quantitatively assessed the walkability of three neighborhoods in Lisbon, Portugal. The comparison of all simulations and the discussion of the results show that the passages improve walkability. It is also noticeable that there is higher walkability in orthogonal urban fabrics than in non-orthogonal fabrics. The results for block size are inconclusive.

Enhancing Command Recognition in Air Traffic Control Through Advanced Classification Techniques

ABSTRACT This paper addresses the persistent challenges in speech processing within the Air Traffic Control (ATC) domain, a field where despite extensive research, issues such as handling noisy environments, accented speech, and the need for strict adherence to standard phraseology continue to undermine conventional language models. Our study employs a hybrid approach that integrates syntactic analysis with advanced machine learning classification algorithms – Logistic Regression, Lagrangian Support Vector Machine, and Naïve Bayes. By mixing and matching algorithms tailored for specific aspects of speech processing, our approach moves away from traditional reliance on a singular integrated system, illustrating through rigorous testing with the ATCOSIM dataset that such a multifaceted strategy markedly improves command recognition accuracy and adapts more effectively to the unique linguistic features of ATC speech. Results highlight the superior performance of Logistic Regression across various command recognition categories, pointing towards a promising direction for future advancements in ATC speech recognition technologies aimed at reducing human workload and increasing automation precision. This paper explores the complexities of the required analysis techniques and underscores the necessity of employing diverse algorithms in the processing pipeline to enhance overall system accuracy.

Subject–verb agreement: Three experiments on Catalan

This study delves into the syntactic parsing abilities of children and infants exposed to Catalan as their first language. Focusing first on ages 3 to 6, we conducted two sentence-picture matching tasks. In experiment 1, 3 to 4-year-old children failed in identifying singular third-person subjects within null-subject sentences, although they performed above chance in all other scenarios, including plural third-person subjects and sentences with overt full DP subjects. This is reminiscent of the results of Pérez-Leroux for Spanish. In experiment 2, with the same design but involving numeral distractors, children’s performance was above chance level across all conditions from age 3 to 4. Then, in experiment 3, we moved to a younger age range with the help of eye-tracking techniques. The findings revealed that infants at 22 months had the ability to parse subject–verb agreement in sentences with third-person null subjects, and at 19 months there was evidence of parsing for third-person plural null subjects. These findings are inconsistent with the perception of children grappling with syntactic agreement computation. We argue that instances of underperformance in subject–verb agreement parsing identified in the literature often stem from task-related and pragmatic issues rather than core syntactic delay. If so, the putative asymmetry between early production of verbal inflection and late comprehension disappears; rather, the results suggest early establishment of matching operations and mastery of language-specific agreement properties before production starts.

Inpatient Corridor Visibility and Care Team Communication.

This article aims to investigate the impact of fine-grained differences in unit layout on care staff corridor communication. Corridor design can be undertaken with greater emphasis on space and cost efficiency or with greater emphasis on affording quality knowledge workspace. Building on prior research on care team communication and inpatient unit design, this study aims to gain a better understanding of how corridor layout properties affect care team communication. This study used space syntax analysis to characterize inpatient unit design, specifically floor-plan layout. Two approaches were used to capture care team communication: behavior mapping with recording of care team member locations and whether they were communicating and the HDR CARE Scale, Inpatient Version. The two units were part of a vertical tower expansion project, and, though constructed at separate times, they maintain the same organizational culture, floor plate, and location of key vertical elements. The newer unit was found to be more visually open using three measures from space syntax. More nursing staff verbal communication was observed on this unit. Across both units, nursing staff were more likely to be observed verbally communicating in locations that were more visually connected (i.e., at locations with larger viewsheds). There were no significant differences between nursing staff self-report on work using the HDR CARE Scale, Inpatient Version. We conclude that care team communication may tend to be encouraged by visual connectivity that can be promoted through floor-plan layout.

A study on the image of Guilin’s urban central space from multidimensional views

In the context of high-quality urban development, strengthening the distinct characteristics of the city itself has become a hot topic. This study focuses on the present Guilin’s urban central space as its research object, aiming to explore the characteristics of Guilin city image through three dimensions: traditional method, spatial syntax, and big data analysis. By exploring the differences presented across these dimensions, the study seeks to analyze the influence of human activities on the spatial form of the city. The investigation reveals that the current development of Guilin’s urban central space manifests a diverse cognitive image characterized by large dispersion, small aggregation, and weak systematicity. The influence of natural landscape on the development of city images in Guilin’s urban central space lies in its ability to facilitate the outlanders’ macroscopic cognition of Guilin’s unique landscape characteristics. However, it also hinders the development of the road network in the urban area, resulting in reduced spatial accessibility and cognitive intensity. In short, Guilin’s urban central space requires systematic integration of the development of multiple images in its future development. This approach entails strengthening the accessibility of public transportation and effectively enhancing the coordination of its unique landscape and natural elements with the development of other urban spaces. Moreover, this study attempts to provide insights into the future planning of cities from the perspective of integrated image development. In addition, it represents a pioneering effort to acquire data and analyze methodologies for cognitive research on city morphology and city image.

Correlative and Free Relative constructions in Odia and English

The present paper investigates multiple Correlative Constructions (CC) in Odia and sketches a combined semantic and syntactic analysis. The paper describes Correlative Constructions and related constructions in Odia, with a view especially to its quantificational systems, one residing in lexical quantifiers, and one in the clause combinations which constitute CCs. Over the last decades, a growing literature has addressed similarities between CCs as instantiated in languages on the Indian subcontinent and types of Free Relatives, e.g., in English, as they occur in positions adjoined to clauses, here to be called Adjoined Free Relatives (AFRs). AFR constructions supplement lexical quantification in English in a parallel way to CCs in Odia, and we explore possibilities of representing CCs and AFR constructions within a common semantico-syntactic frame of analysis. We show how the quantificational effects of CCs can be derived from their character as relative constructions, residing in what we call co-targeted predicates, as opposed to lexical encoding of quantificational meaning through items such as ‘each’, ‘every’ and the like. We thereby describe two distinct strategies for obtaining partially similar quantificational effects, a finding which applies to CC/AFR constructions cross-linguistically.

Research on Aspect-Level Sentiment Analysis Based on Adversarial Training and Dependency Parsing

Aspect-level sentiment analysis is used to predict the sentiment polarity of a specific aspect in a sentence. However, most current research cannot fully utilize semantic information, and the models lack robustness. Therefore, this article proposes a model for aspect-level sentiment analysis based on a combination of adversarial training and dependency syntax analysis. First, BERT is used to transform word vectors and construct adjacency matrices with dependency syntactic relationships to better extract semantic dependency relationships and features between sentence components. A multi-head attention mechanism is used to fuse the features of the two parts, simultaneously perform adversarial training on the BERT embedding layer to enhance model robustness, and, finally, to predict emotional polarity. The model was tested on the SemEval 2014 Task 4 dataset. The experimental results showed that, compared with the baseline model, the model achieved significant performance improvement after incorporating adversarial training and dependency syntax relationships.

Decoding IELTS writing performance: Grammatical Clauses mastery by Universitas Ahmad Dahlan students

Indonesian learners of English produced ungrammaticalities in their writing for the International English Language Testing System (IELTS) and faced complicated writing score descriptors for self-evaluations and improvements. These problems were observed among students at Universitas Ahmad Dahlan during their preparatory course for IELT. To address this issue, this study focused on identifying some rules in grammar, the grammatical and ungrammatical clauses based on subject-verb (S + V) structures in their writing performances and measuring the proportions of the two clause types in accordance with the performance’s score variation. Both qualitative and quantitative methods were employed. To do so, the study conducted archival research by retrieving data from the teacher's course documents. The archived handwritten writing performances were transcribed manually into digital texts. It employed syntactic analysis to classify the clauses as either grammatical or ungrammatical. It converted the numbers of each clause type per performance into percentages for comparison, and grouped them according to the different scores as the variable. The results showed that the participants produced predominantly simple grammatical clauses to minimize failures when generating more complex structures. To achieve the minimum score of 6 in the writing section, the proportion of grammatically correct and incorrect clauses should be above 47% and below 53%, respectively. These findings offer valuable insights into predicting scores, especially concerning the dimension of 'grammatical range and accuracy in the writing assessment, which benefit both learners and educators.

Evaluating NLP models with written and spoken L2 samples

The use of natural language processing tools such as part-of-speech taggers and syntactic parsers are increasingly being used in studies of second language (L2) proficiency and development. However, relatively little work has focused on reporting on the accuracy of these tools or optimizing their performance in L2 contexts. While some studies reference the published overall accuracy of a particular tool or include a small-scale accuracy analysis, very few (if any) studies provide a comprehensive account of the performance of taggers and parsers across a range of written and spoken registers. In this study, we provide a large-scale accuracy analysis of popular taggers and parsers across L1 and L2 written and spoken texts, both when default and L2-optimized models are used. Accuracy is examined both at the feature level (e.g., identifying adjective-noun relationships) and the text level (e.g., mean mutualinformation scores). The results highlight the strength and weaknesses of these tools.

Seismic response of structures with friction pendulum inerter system (FPIS) under near-fault earthquakes

Near-fault ground motions with high acceleration peaks and long-period velocity pulses pose a serious threat to the reliability of engineering structures. To reduce the displacement of isolation layer and improve the seismic performance of superstructure under the near-fault ground motions, a composite isolation system, which is composed of friction pendulum system (FPS) and inerter system, namely FPIS, was used in this paper. Based on D’Alembert’s principle, the nonlinear motion equations of a base-isolated structure with FPIS were established. The damping effect of two different mechanical layouts of FPIS subsystems, i.e, series-parallel inerter system-I-FPS(SPIS-I-FPS) or series-parallel inerter system-II-FPS(SPIS-II-FPS), were investigated in this study. The strong nonlinearity of FPIS was considered, and the inerter system parameters were designed based on the principle of maximum damping enhancement. The fourth-order Runge-Kutta approach was used to solve the dynamic response of a multi-degree-of-freedom system under seismic excitations. The effectiveness of FPIS was verified by comparing the isolation layer displacement and the acceleration of superstructure calculated by MATLAB with the friction pendulum system and viscous damper (FPS-VD). The nonlinear time history analysis results indicate that within a certain additional damping range, the SPIS-I-FPS subsystem performs effectively than SPIS-II-FPS in reducing the superstructure acceleration and isolation layer displacement. To increase the energy dissipation efficiency of structures, it is recommended to increase the design parameters of the inerter system and control the friction coefficient of FPS within the range of 0.05∼0.10.

Incorporating knowledge for joint Chinese word segmentation and part-of-speech tagging with SynSemGCN

PurposeRecent trends have shown the integration of Chinese word segmentation (CWS) and part-of-speech (POS) tagging to enhance syntactic and semantic parsing. However, the potential utility of hierarchical and structural information in these tasks remains underexplored. This study aims to leverage multiple external knowledge sources (e.g. syntactic and semantic features, lexicons) through various modules for the joint task.Design/methodology/approachWe introduce a novel learning framework for the joint CWS and POS tagging task, utilizing graph convolutional networks (GCNs) to encode syntactic structure and semantic features. The framework also incorporates a pre-defined lexicon through a lexicon attention module. We evaluate our model on a range of public corpora, including CTB5, PKU and UD, the novel ZX dataset and the comprehensive CTB9 dataset.FindingsExperimental results on these benchmark corpora demonstrate the effectiveness of our model in improving the performance of the joint task. Notably, we find that syntax information significantly enhances performance, while lexicon information helps mitigate the issue of out-of-vocabulary (OOV) words.Originality/valueThis study introduces a comprehensive approach to the joint CWS and POS tagging task by combining multiple features. Moreover, the proposed framework offers potential adaptability to other sequence labeling tasks, such as named entity recognition (NER).

Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities

Code models have made significant advancements in code intelligence by encoding knowledge about programming languages. While previous studies have explored the capabilities of these models in learning code syntax, there has been limited investigation on their ability to understand code semantics. Additionally, existing analyses assume the number of edges between nodes at the abstract syntax tree (AST) is related to syntax distance, and also often require transforming the high-dimensional space of deep learning models to a low-dimensional one, which may introduce inaccuracies. To study how code models represent code syntax and semantics, we conduct a comprehensive analysis of 7 code models, including four representative code pre-trained models (CodeBERT, GraphCodeBERT, CodeT5, and UnixCoder) and three large language models (StarCoder, CodeLlama and CodeT5+). We design four probing tasks to assess the models’ capacities in learning both code syntax and semantics. These probing tasks reconstruct code syntax and semantics structures (AST, CDG, DDG and CFG) in the representation space. These structures are core concepts for code understanding. We also investigate the syntax token role in each token representation and the long dependency between the code tokens. Additionally, we analyze the distribution of attention weights related to code semantic structures. Through extensive analysis, our findings highlight the strengths and limitations of different code models in learning code syntax and semantics. The results demonstrate that these models excel in learning code syntax, successfully capturing the syntax relationships between tokens and the syntax roles of individual tokens. However, their performance in encoding code semantics varies. CodeT5 and CodeBERT demonstrate proficiency in capturing control and data dependencies, while UnixCoder shows weaker performance in this aspect. We do not observe LLMs generally performing much better than pre-trained models. The shallow layers of LLMs perform better than their deep layers. The investigation of attention weights reveals that different attention heads play distinct roles in encoding code semantics. Our research findings emphasize the need for further enhancements in code models to better learn code semantics. This study contributes to the understanding of code models’ abilities in syntax and semantics analysis. Our findings provide guidance for future improvements in code models, facilitating their effective application in various code-related tasks.

Language transfer in L2 academic writings: a dependency grammar approach.

Dependency distance (DD) is an important factor in language processing and can affect the ease with which a sentence is understood. Previous studies have investigated the role of DD in L2 writing, but little is known about how the native language influences DD in L2 academic writing. This study is probably the first one that investigates, though a large dataset of over 400 million words, whether the native language of L2 writers influences the DD in their academic writings. Using a dataset of over 2.2 million abstracts of articles downloaded from Scopus in the fields of Arts & Humanities and Social Sciences, the study analyzes the DD patterns, parsed by the latest version of the syntactic parser Stanford Corenlp 4.5.5, in the academic writing of L2 learners from different language backgrounds. It is found that native languages influence the DD of English L2 academic writings. When the mean dependency distance (MDD) of native languages is much longer than that of native English, the MDD of their English L2 academic writings will be much longer than that of English native academic writings. The findings of this study will deepen our insights into the influence of native language transfer on L2 academic writing, potentially shaping pedagogical strategies in L2 academic writing education.

A domain knowledge-informed design space exploration methodology for mechanical layout design

Layout designs have a large number of design variables and various physical constraints. Conventional design exploration approaches are time-consuming and may require human intervention. A unique feature about physical layout designs is the availability of domain knowledge, which can be utilised to speed up the design process. In this paper, we propose a generative deep learning-based design space exploration (DSE) methodology that is capable of learning design constraints in the layout design without explicit supervision. Moreover, it can incorporate domain knowledge in the generated layouts, thereby speeding up the design process. This is realised by constructing a layout generation variational autoencoder (LGVAE) model, which uses a latent space as an interface to generate the layouts. By training the LGVAE model, significantly lower-dimensional representations can be learned compared to the original dimensionality of the design space. Therefore, the number of design variables is greatly reduced. We showcase the performance of the proposed DSE approach by solving the heat source layout design problem encountered in thermal management of chips. Experiments demonstrate that the LGVAE model is capable of generating compressed latent representations that capture the characteristics of the input samples, which makes the DSE cost-effective.

Analysis of factors influencing small-scale agricultural product prices from the perspective of the online public—a case study of China

In recent years, the high-frequency abnormal price fluctuations of small-scale agricultural products have been detrimental to market stability. The traditional method based on statistical data of production factors can no longer accurately predict abnormal price fluctuations. Therefore, this study takes scallions as an example and extracts the influencing factors of their prices based on the LDA-NLP-TVP-VAR model from massive network information, and analyzes the causes and changing characteristics of price fluctuations from four perspectives: market supply, economic environment and market sentiment, and attention. Initially, the study employs an LDA topic model to extract factors from online sentiment data over the past four years. Subsequently, using the SO-PMI algorithm to expand domain-specific lexicons and employing semantic and sentiment analysis with syntactic parsing, the study aims to improve the accuracy of sentiment quantification in text analysis. Furthermore, cointegration and Granger causality tests validate the significant impact of these factors on scallion price fluctuations, mitigating spurious regression issues. Finally, by employing a TVP-VAR model, the study compares the impulse responses and shock magnitudes of various factors in different time and spatial dimensions. It also delves into the mechanisms of heterogeneous impacts and trends in scallion prices, considering the corresponding socio-economic context and public sentiment events within specific time windows, thereby providing valuable decision-making insights for the healthy development of agricultural markets.

Verb-centered means of expressing self-presentation in the English-language job-hunting discourse

This article presents a semantic, syntactic, and contextual analysis of the language means of self-presentation, conceptualized as the dominant speech strategy in the job-hunting discourse. The scope of our research is limited to the written variety of texts expressed via the genres of CVs/resumes and cover letters. The material was taken from English-language job-hunting resources: Balance, Career Addict, Career Blog, Indeed, Resume.io, StandOut CV, and Zety. It is stipulated that self-presentation can be viewed as a system of interrelated speech tactics: self-nomination, self-description, presentation of professional achievements, description of professional responsibilities, and identification with an ideal candidate type. Each tactic has its unique semantic profile. All self-presentation tactics roughly fall into two major categories—nominative and verb-centered. The latter category, which incorporates the tactics of presentation of professional achievements and description of professional responsibilities, was the primary focus of this research. A careful semantic and syntactic analysis revealed that active verbs with two-complement (in some cases, three-complement) valency are the core of the aforementioned tactics. This leads us to believe that the actant situation almost always includes the agent and theme, sometimes the benefactive. As a result of semantic analysis, lexical units were classified according to their sentimental modality—positive, negative, or neutral. Our research documented the prevalence of positive evaluation semantics in self-presentation units. A syntactic analysis was employed to identify the formal aspects of sentences containing self-presentation. The analysis showed that most sentence structures fall into three basic types of syntax reduction: a) subject omission ([I] perform regular check-ups); b) omission of the subject and the link verb ([I am] professionally trained); c) the omission of the subject and the verb of possession ([I have] numerous awards). The authors conclude that the self-presentation blueprint of verb-centered constructions identified in this article persists across all verb-centered tactics proving to be an effective means of constructing written job-hunting discourse.

KAZAKH DIGITAL TERMINOLOGY: EXPERIENCE IN DEVELOPING A TERMINOLOGICAL SUBCORPUS (metatext and terminology markup parameters)

The article is devoted to scientific-theoretical and applied-practical issues (development of metatext and terminological markings) of the creation of a terminological subcorpus, which is part of the National Corpus of the Kazakh Language (NCKL).In the course of the study, the following results were achieved in the development of the terminological subcorpus of the NCKL: 1) scientific texts included in the terminological corpus base have the following meta-text markups: Source, Text type, Text Title, Text Style, Keywords of the text, Field of science, Text language, Number of pages of text, Text graphics, Number of usages, Age of the audience, Subcorpus, Date the text was entered, Markup; 2) terminological terms of various branches of science were assigned terminological markings, including the following parameters: variants of the term in Kazakh and Russian, the etymology of the term and the words that make up the terminological combination, and its equivalent in the Kazakh language, the definition of each term in the Kazakh language, the source from which it was obtained definition of the term, area(s) of application of the term, author of the term, composition of the term, approved version of the term, year of its approval, translation of the term in industry terminology dictionaries, use of the term in legislation (translation and variants).The theoretical significance: clarifies and complements the scientific foundations of the compilation of bilingual and multilingual industry, explanatory terminological dictionaries, the development of electronic dictionaries, contributes to the development of applied terminology and terminography.The practical significance: it can be used in lexicographic practice in the process of developing thematic and sectoral terminological subcorps and during the improvement of the structure and content of various terminological dictionaries.The research used methods of collection, sorting, systematization, translation, diachronic, factual, lexicographic comparison and matching, structural (morphological, syntactic) analysis.

Enhancing spatial accessibility of tourist spots in Al Kharj city through space syntax analysis

The tourism industry plays a pivotal role in the economic growth and cultural enrichment of cities. Urban regeneration has been considered as a key focus of urban tourism development. This study investigates the application of space syntax as an analytical and design tool in urban regeneration efforts, specifically focusing on Al Kharj City, Saudi Arabia, as a burgeoning tourist destination. The research aims to enhance the spatial accessibility of tourist spots in Al Kharj through space syntax analysis, addressing existing gaps in understanding and optimizing accessibility within the city. By integrating space syntax analysis into urban planning processes, this study seeks to inform stakeholders about the importance of spatial accessibility in enhancing the visitors’ experience and fostering sustainable urban development. Through a multi-scale approach and advanced analytical techniques, the study provides insights into the complex relationships between spatial structures, economic activities, and functional distributions, offering recommendations for improving accessibility and optimizing the spatial distribution of tourist services in Al Kharj. Integrating spatial analysis into urban planning, enhancing connectivity and integration, promoting community involvement, addressing spatial disparities, and implementing evidence-based interventions could result in a more accessible and inclusive urban environment for the enhancement of urban tourism.