Syntax Structure Research Articles

NLPLego: Assembling Test Generation for Natural Language Processing Applications

With the development of Deep Learning, Natural Language Processing (NLP) applications have reached or even exceeded human-level capabilities in certain tasks. Although NLP applications have shown good performance, they can still have bugs like traditional software and even lead to serious consequences. Inspired by Lego blocks and syntax structure analysis, we propose an assembling test generation method for NLP applications or models and implement it in NLPLego . The key idea of NLPLego is to assemble the sentence skeleton and adjuncts in order by simulating the building of Lego blocks to generate multiple grammatically and semantically correct sentences based on one seed sentence. The sentences generated by NLPLego have derivation relations and different degrees of variation. These characteristics make it well-suited for integration with metamorphic testing theory, addressing the challenge of test oracle absence in NLP application testing. To validate NLPLego , we conduct experiments on three commonly used NLP tasks (i.e., machine reading comprehension, sentiment analysis, and semantic similarity measures), focusing on the efficiency of test generation and the quality and effectiveness of generated tests. We select five advanced NLP models and one popular industrial NLP software as the tested subjects. Given seed tests from SQuAD 2.0, SST, and QQP, NLPLego successfully detects 1,732, 3,140, and 261,879 incorrect behaviors with around 93.1% precision in three tasks, respectively. The experiment results show that NLPLego can efficiently generate high-quality tests for multiple NLP tasks to detect erroneous behaviors effectively. In the case study, we analyze the testing results provided by NLPLego to obtain intuitive representations of the different NLP capabilities of the tested subjects. The case study confirms that NLPLego can provide developers with clarity on the direction to improve NLP models or applications, laying the foundation for enhancing performance.

FMCF: A fusing multiple code features approach based on Transformer for Solidity smart contracts source code summarization

A smart contract is a software program executed on a blockchain, designed to facilitate functionalities such as contract execution, asset administration, and identity validation within a secure and decentralized ecosystem. Summarizing the code of Solidity smart contracts aids developers in promptly grasping essential functionalities, thereby enhancing the security posture of Ethereum-based projects. Existing smart contract code summarization works mainly use traditional information retrieval and single code features, resulting in suboptimal performance. In this study, we propose a fusing multiple code features (FMCF) approach based on Transformer for Solidity summarization. First, FMCF created contract integrity modeling and state immutability modeling in the data preprocessing stage to process and filter data that meets security conditions. At the same time, FMCF retains the self-attention mechanism to construct the Graph Attention Network (GAT) encoder and CodeBERT encoder, which respectively extract multiple feature vectors of the code to ensure the integrity of the source code information. Furthermore, the FMCF uses a weighted summation method to input these two types of feature vectors into the feature fusion module for fusion and inputs the fused feature vectors into the Transformer decoder to obtain the final smart contract code summarization. The experimental results show that FMCF outperforms the standard baseline methods by 12.45% in the BLEU score and maximally preserves the semantic information and syntax structures of the source code. The results demonstrate that the FMCF can provide a good direction for future research on smart contract code summarization, thereby helping developers enhance the security of development projects.

DNA N-gram analysis framework (DNAnamer): A generalized N-gram frequency analysis framework for the supervised classification of DNA sequences

In 1948, Claude Shannon published a mathematical system describing the probabilistic relationships between the letters of a natural language and their subsequent order or syntax structure. By counting unique, reoccurring sequences of letters called N-grams, this language model was used to generate recognizable English sentences from N-gram frequency probability tables. More recently, N-gram analysis methodologies have been successfully applied to address many complex problems in a variety of domains, from language processing to genomics. One such example is the common use of N-gram frequency patterns and supervised classification models to determine authorship and plagiarism. In this paradigm, DNA is a language model where nucleotides are analogous to the letters of a word and nucleotide N-grams are analogous to the words of a sentence. Because DNA contains highly conserved and identifiable nucleotide sequence frequency patterns, this approach can be applied to a variety of classification and data reduction problems, such as identifying species based on unknown DNA segments. Other useful applications of this methodology include the identification of functional gene elements, microorganisms, sequence contamination, and sequencing artifacts. To this end, I present DNAnamer, a generalized and extensible methodological framework and analysis toolkit for the supervised classification of DNA sequences based on their N-gram frequency patterns.

KONSTRUKSI BERITA PERNIKAHAN SIRI DI MEDIA ONLINE:

The phenomenon of unregistered marriage raises pros and cons in a multicultural society. This research uses a qualitative descriptive approach with framing analysis by Zhongdang Pan and Gerald M. Kosicki. Identifying through the four structures of syntax, script, thematic, and rhetorical. The final step is examined from the perspective of Islamic journalism. This research aims to determine the construction of news in the online media Kompas.com and Republika.co.id. Next, it is analyzed using an Islamic journalism perspective. The object of the research is 6 news stories about serial marriages in the period March-November 2022. This is to see the differences in media construction in framing news. The results of this research are as follows: first, in the construction aspect Kompas.com and Republika.co.id as a whole both convey information about unregistered marriages which should be avoided by the public. Second, differences in media construction are influenced by respective ideologies. Kompas.com, which has nationalist characteristics, has widely reported on the negative impacts of unregistered marriages. Republika.co.id, which has an Islamic ideology, raised the news with a religious approach. On aspects of Islamic journalism, Kompas.com has not fully implemented the values ​​of the concept of Islamic journalism. Meanwhile, Republika.co.id as a whole has prioritized Islamic concepts over news construction.

Prosodic Weight Effect on Focal Accent of Standard Chinese

This study investigates the phonetic performance of narrow information focus, as compared to broad focus, within different sentential positions of the Chinese Subject-Verb-Object (S+V+O) syntax structure. Three acoustic parameters of focus, including the pitch range ratio, duration ratio, and intensity ratio, were taken to explore the ranking among these parameters and to analyse the impact of focus position on their interrelationships. The results showed that (1) Chinese, as a tonal language, exhibits a prosodic ranking characterized by duration > pitch range > intensity; (2) Chinese speakers tend to emphasize verbs within the S+V+O syntax structure, particularly when verbs are positioned in the middle of sentences. The prosodic ranking observed was Fmiddle > Finitial > Ffinal; (3) Pitch range and duration display interdependence as acoustic parameters, whereas intensity stands as an independent parameter. These results contribute to a deeper understanding of phonetic nuances within Chinese prosody, shedding light on the intricate interplay between focus, syntax, and prosody.

DualGCN: Exploring Syntactic and Semantic Information for Aspect-Based Sentiment Analysis.

The task of aspect-based sentiment analysis aims to identify sentiment polarities of given aspects in a sentence. Recent advances have demonstrated the advantage of incorporating the syntactic dependency structure with graph convolutional networks (GCNs). However, their performance of these GCN-based methods largely depends on the dependency parsers, which would produce diverse parsing results for a sentence. In this article, we propose a dual GCN (DualGCN) that jointly considers the syntax structures and semantic correlations. Our DualGCN model mainly comprises four modules: 1) SynGCN: instead of explicitly encoding syntactic structure, the SynGCN module uses the dependency probability matrix as a graph structure to implicitly integrate the syntactic information; 2) SemGCN: we design the SemGCN module with multihead attention to enhance the performance of the syntactic structure with the semantic information; 3) Regularizers: we propose orthogonal and differential regularizers to precisely capture semantic correlations between words by constraining attention scores in the SemGCN module; and 4) Mutual BiAffine: we use the BiAffine module to bridge relevant information between the SynGCN and SemGCN modules. Extensive experiments are conducted compared with up-to-date pretrained language encoders on two groups of datasets, one including Restaurant14, Laptop14, and Twitter and the other including Restaurant15 and Restaurant16. The experimental results demonstrate that the parsing results of various dependency parsers affect their performance of the GCN-based models. Our DualGCN model achieves superior performance compared with the state-of-the-art approaches. The source code and preprocessed datasets are provided and publicly available on GitHub (see https://github.com/CCChenhao997/DualGCN-ABSA).

The Impact Of Applied Linguistics On The Field Of Grammar: A Comparative Analytical Study With The Letter 'و' (Waw) As A Model/ أثر اللغويات التطبيقية في علم النحو: حرف الواو أنموذجًا دراسة تحليلية مقارنة

Language serves as a crucial tool for human interaction. It transcends its basic structure of words, rules, and syntax, reflecting the complex subtleties of culture, history, and the social development of its users. Within the study of language, applied linguistics stands out as an essential discipline that delves deeper than theoretical discussions. It critically examines how language is utilized, its manifestations in various contexts, and its multifaceted applications in human communications across different fields. Central to this research is the exploration of the transformative influence of applied linguistics on the domain of grammar. This study places particular emphasis on the Arabic letter 'و' (Waw). Given its frequent occurrence and versatile role in texts, ranging from the esteemed Quran to classical Arabic literature and detailed grammatical discussions, the comprehensive and precise understanding of the letter 'و' is of utmost importance. Such understanding facilitates both rigorous textual analysis and profound comprehension of linguistic constructs within the Arabic language. To discern the depth of this influence, this research adopts a comparative methodology. It juxtaposes the time-honored traditional grammatical approaches with contemporary perspectives offered by applied linguistics. Through this comparative lens, the study sets out to achieve two primary objectives: firstly, to identify how applied linguistics might present alternative or even more enriched interpretations of 'و' distinct from classical views; and secondly, to elucidate the tangible implications of these interpretations in practical, real-world linguistic applications. In culmination, this rigorous investigation serves a dual purpose. Not only does it shed light on the nuanced roles and interpretations of the Arabic letter 'و', but it also positions applied linguistics as a beacon guiding the future trajectory of grammatical studies. The research accentuates the indispensability of a symbiotic relationship between traditional grammar and modern applied linguistic techniques, advocating for their combined use in achieving a holistic and evolved understanding of the dynamic Arabic language system.

RESTBERTa: a Transformer-based question answering approach for semantic search in Web API documentation

To enable machines to process state-of-practice Web API documentation, we propose a Transformer model for the generic task of identifying a Web API element within a syntax structure that matches a natural language query. We solve this semantic-search task with Transformer-based question answering and demonstrate the applicability of our approach to two different tasks, namely the discovery of endpoints and the identification of parameters in payload schemas. With samples from 2321 OpenAPI documentation, we prepare different datasets and fine-tune pre-trained BERT models to these two tasks. We evaluate the generalizability and the robustness of our fine-tuned models. We achieve accuracies of 81.95% for the parameter-matching and 88.44% for the endpoint-discovery task.

A Flexible Algorithm Design of Spatial Scalability for Real-time Surveillance Applications

The Surveillance Video and Audio Coding )SVAC( working group is currently developing the third-generation video compression standard, SVAC 3.0. To extend this standard, this paper proposes a spatial scalable coding Scalable Surveillance Video Coding )SSVC( framework, so that the SVAC3.0 video stream can gracefully adapt to different transmission bandwidth limitations and the requirements of decoding hardware, while keep the quality of the reconstructed image without degradation. In order to achieve the scalability of hardware implementation, SSVC designs a flexible reference frame marking and usage scheme, so that the enhanced layer coding does not directly depend on the basic layer, and effectively reduces the coding coupling between layers. SSVC improves the motion vector prediction method, effectively utilizes the encoding information of the base layer, and is mainly compatible with SVAC3.0 syntax structures. In addition, SSVC provides several different operational modes to adapt to various application scenarios and achieve an optimal trade-off between coding efficiency and complexity. The performance comparisons among simulcast stream and SSVC, single-layer stream and SSVC enhancement layer, as well as experimental data for different operational modes are provided. The coding efficiency and computational complexity are also analyzed.

End-to-end comparative opinion quintuple extraction as bipartite set prediction with dynamic structure pruning

Comparative Opinion Quintuple Extraction (COQE) is an emerging task focused on identifying comparative relationships within sentence-level comments. COQE involves extracting a quintuple that encapsulates a specific comparative relation, encompassing a subject, object, comparative aspect, opinion, and preference. Previous approaches to COQE relied on pipeline methods, which unfortunately suffered from error propagation during the sequential process. In our work, we present an efficient and highly effective end-to-end solution for COQE, improving three key aspects: encoding, decoding, and learning. First, in terms of feature encoding, we introduce external syntactic dependency structural features to enhance the modeling of opinion elements. We also propose a novel dynamic structural pruning (DSP) mechanism to refine and optimize the raw syntax structure, ensuring alignment with the end task. Second, regarding decoding, we address the challenge of non-coherent relationships among quintuple constituents by formulating COQE as a set prediction problem. We employ a non-autoregressive decoding scheme to simultaneously generate all possible quintuples in parallel, increasing decoding speed while retaining the advantages of a generative approach. Finally, from a learning perspective, we introduce three advanced training targets: bipartite matching learning loss, cross-element contrastive loss, and mention boundary learning loss. These targets promote interaction learning between quadruples and elements. Through extensive experiments on three benchmark datasets, our end-to-end COQE system demonstrates significant performance improvements over both current pipeline baselines and state-of-the-art joint systems by 2 to 3 points. We also demonstrate that our proposed dynamic pruning strategy over syntactic dependency features is important to the overall performance, and meanwhile our system is capable of producing explainable predictions. Furthermore, the non-autoregressive decoding greatly enhances the inference efficiency, and multiple advanced training losses help achieve better learning process.

PyScribe–Learning to describe python code

AbstractCode comment generation, which attempts to summarize the functionality of source code in textual descriptions, plays an important role in automatic software development research. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on abstract syntax trees (ASTs). However, they can not well capture both the long‐distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present a prototype tool titled PyScribe, which extends the Transformer model to a new encoder‐decoder‐based framework. Particularly, the triplet position is designed and integrated into the node‐level and edge‐level structural features of AST for producing Python code comments automatically. This paper, to the best of our knowledge, makes the first effort to model the edges of AST as an explicit component for improved code representation. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. Moreover, the captured node and edge features go through a two‐stage decoding process to yield higher qualified comments. To evaluate the effectiveness of PyScribe, we resort to a large dataset of code‐comment pairs by mining Jupyter Notebooks from GitHub, for which we have made it publicly available to support further studies. The experimental results reveal that PyScribe is indeed effective, outperforming the state‐ofthe‐art by achieving an average BLEU score (i.e., av‐BLEU) of 0.28.

The Source Code Comment Generation Based on Deep Reinforcement Learning and Hierarchical Attention

Code summarization provides the main aim described in natural language of the given function, it can benefit many tasks in software engineering. Due to the special grammar and syntax structure of programming languages and various shortcomings of different deep neural networks, the accuracy of existing code summarization approaches is not good enough. This work proposes to adopt the hierarchical attention mechanism to enable the code summarization framework to translate three representations of source code to the hidden spce and then it injects them into a deep reinforcement learning model to enhance the performance of code summarization. We conduct a few of experiments, and the results of which prove that the proposed approaches can obtain better accuracy compared with the baseline approaches.

The Source Code Comment Generation based on Abstract Syntax Tree

Code summarization provides the main aim described in natural language of the given function; it can benefit many tasks in software engineering. Due to the special grammar and syntax structure of programming languages and various shortcomings of different deep neural networks, the accuracy of existing code summarization approaches is not good enough. We proposes to use abstract syntax trees for source code summarization .Our solution is inspired by recent advances in neural machine translation, as well as an approach called SBT by Hu et al. We evaluate our approach using the automated metric BLEU and compare it to other relevant models.

Grammar in Language and Listening Acquisition

The existence and self-realization of humans are possible due to language, language faculty, and listening acquisition. Anything animate or inanimate perceived in the external world such as an object, phenomenon, entity, etc. acquires a value due to its meaning (semantics). Due to the semantic and syntax structure of a language and its universal dimension, listening plays a key role in the acquisition of language and grammar rules. The present study aimed to discuss the significance of the acquisition of grammar rules in language and listening acquisition based on current literature. The present study data was based on a systematic literature review. In the study, studies on the relationships among language, language acquisition, listening and grammar were reviewed and the acquisition of grammar rules during language and listening acquisition was discussed. The study concluded that listening was effective in the acquisition of grammar rules.

Exploring the Influence of L1 Chinese on L2 English Acquisition

The impact of L1 Chinese on L2 English acquisition has received more attention as more Chinese students study English as a second language. This paper reviews previous research on Second Language Acquisition (SLA) and introduces some factors that can influence language transfer. The writers focus on three linguistic aspects, syntax, lexicon, and phonology, to explore how English as Second Language (ESL) learners L1 Chinese has influenced their English acquisitions. Results found that: 1) Syntax: The syntax structure includes many factors influencing Chinese learners English acquisition, such as subjects, verbs, and adverbs. English requires an issue while Chinese tend to omit the subject; English has a verb conjugation system while Chinese does not; and English adverbs can be placed in different positions according to the type while Chinese adverbs are mainly set in the middle; 2) Lexicon: Lexical errors may occur when ESL learners may make errors when they meet the differences in linguistic features such as polysemy, part of speech and word collocations between the two languages.; 3) Phonology: As Chinese is a tonal language and English is a non-tonal language, their tones of phonology are, to some extent, different, which might cause ESL learners confusion. These features of Chinese and English differ, and how these disparities have prevented positive language transfer from Chinese to English is explored. As a result, this study can inform second language instructors of the distinctions between the two languages and offer resources for future instruction of ESL students.

Summarizing source code through heterogeneous feature fusion and extraction

Code summarization, which seeks to automatically produce a succinct natural-language description to summarize the functionality of source code, plays an essential role in maintaining the software. Currently, plentiful approaches have been proposed to first encode the source code based on its Abstract Syntax Tree (AST), and then decode it into a textual summary. However, most existing works interpret the AST-based syntax structure as a homogeneous graph, without discriminating the different relations between graph nodes (e.g., the parent–child and sibling relations) in a heterogeneous way. To mitigate this issue, this paper proposes HetCoS to extract the syntactic and sequential features of source code by exploring its inherent heterogeneity for code summarization. Specifically, we first build a Heterogeneous Code Graph (HCG) that fuses the syntax structure and code sequence with eight types of edges/relations designed between graph nodes. Moreover, we present a heterogeneous graph neural network for capturing the diverse relations in HCG. The represented HCG is then fed into a Transformer decoder, followed by a multi-head attention-based copying mechanism to support high-quality summary generation. Extensive experiments on the major Java and Python datasets illustrate the superiority of our approach over sixteen state-of-the-art baselines. To promote reproducibility studies, we make the implementation of HetCoS publicly accessible at https://github.com/GJCEXP/HETCOS.

Discourse and Racism in "Hidden Figures"

This research aims to describe discourse and racism in the Hidden Figures movie using discourse and racism theory based on Dijk’s approach. The results showed that the ten aspects of Dijk's theory of discourse and racism could be found in several characters such as speech, expressions, and actions that refer to the use of discourse and racism in the Hidden Figures movie. Two aspects of discourse and racism are most dominantly depicted in this movie, namely aspects of nonverbal structure and aspects of syntax. In the nonverbal structure aspect, the visible form of racism is the depiction of facial expressions such as anger, annoyance, and dislike as well as body movements refusing to interact for a long time with black people. Discourse and racism in the Hidden Figures movie also display racist behavior towards black people as a reflection of a system of prejudice, namely negative emotional reactions and stereotypes, namely judgments or assumptions based on groups and people's characteristics. This is illustrated by the experiences of Katherine, Dorothy, and Mary regarding the separation of public facilities such as toilets, libraries, and education.

Syntax of European Union Law

The article investigates the significance of syntax in the multilingual EU law. It attempts to respond to the question whether syntax is apt to contribute to the uniformity of that law and how, with regard to this function, it relates to the (widely disputed yet uncontested) semantic and pragmatic methods of achieving such a uniformity. In order to respond to this question, the article firstly, recalls fundamental concepts which would help conceptualize the endeavour and, secondly, presents examples of analysis of syntax arrangements which can be deemed representative for the study of the said problem of contribution. The study finds that EU law (expressed in 24 official languages which have equal authentic status) relies on diversified syntax of its respective constitutive languages. Syntax structures used in respective language versions of EU law represent narrower, law-specific, form of syntax structures available in these languages. Its specificity is determined mostly by legislative traditions of respective EU member states. Syntactic structures of EU law produced in different official languages do not represent a single pattern because of diversified mode of producing illocutionary value of provisions in which strongly idiomatic modal verbs (modal operators) and even special modal structures (not necessarily containing modal verbs) are used to express legal norms. They also differ when it comes to their law-specific compositionality, i.e. flexibility of different syntax structure to produce the same meaning and, in the same time, to preserve their genre/register informational value. Notwithstanding, these structures have well pronounced and system-significant common features within respective Germanic, Romanic, and Slavic families of languages in which EU law is mostly reproduced. The sentence structure and the relevant register of EU law provisions are the same for respective versions of EU texts of law expressed in the languages belonging to the respective three language families which were examined for the sake of this study. These common features are re-enforced by the synoptic mode of producing EU law which imposes formal resemblance of provisions reproduced in respective EU official languages to each other. The multilingual EU legislator also uses patterns which grant legal text the relevant register, yet its specific EU character ultimately transpires through semantic aspects of EU texts rather than their mere syntax. The unity of the system is achieved most strongly through the EU specific interpretation, where the teleological methods (which can be conceived as a language means to achieve EU law goals and objectives at the pragmatic level) are of utmost importance. Thus, there is no EU-specific syntax which would, as such, contribute to unity of EU law. Instead, unity is achieved in the area of semantics and pragmatics. The well exposed anaphoric character of EU law (as any other type of law) may contribute to narrowing down possibility for differentiation of respective language versions of EU law.

Syntactic Structure-Enhanced Dual Graph Convolutional Network for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ALSC) is a fine-grained sentiment analysis task that aims to predict the sentiment of the given aspect in a sentence. Recent studies mainly focus on using the Graph Convolutional Networks (GCN) to deal with both the semantics and the syntax of a sentence. However, the improvement is limited since the syntax dependency trees are not aspect-oriented and the exploitation of syntax structure information is inadequate. In this paper, we propose a Syntactic Structure-Enhanced Dual Graph Convolutional Network (SSEDGCN) model for an ALSC task. Firstly, to enhance the relation between aspect and its opinion words, we propose an aspect-wise dependency tree by reconstructing the basic syntax dependency tree. Then, we propose a syntax-aware GCN to encode the new tree. For semantics information learning, a semantic-aware GCN is established. In order to exploit syntactic structure information, we design a syntax-guided contrastive learning objective that makes the model aware of syntactic structure and improves the quality of the feature representation of the aspect. The experimental results on three benchmark datasets show that our model significantly outperforms the baseline models and verifies the effectiveness of our model.

Unsupervised Paraphrasing under Syntax Knowledge

The soundness of syntax is an important issue for the paraphrase generation task. Most methods control the syntax of paraphrases by embedding the syntax and semantics in the generation process, which cannot guarantee the syntactical correctness of the results. Different from them, in this paper we investigate the structural patterns of word usages termed as the word composable knowledge and integrate it into the paraphrase generation to control the syntax in an explicit way. This syntax knowledge is pretrained on a large corpus with the dependency relationships and formed as the probabilistic functions on the word-level syntactical soundness. For the sentence-level correctness, we design a hierarchical syntax structure loss to quantitatively verify the syntactical soundness of the paraphrase against the given dependency template. Thus, the generation process can select the appropriate words with consideration on both semantics and syntax. The proposed method is evaluated on a few paraphrase datasets. The experimental results show that the quality of paraphrases by our proposed method outperforms the compared methods, especially in terms of syntax correctness.