Journal articles on the topic 'Deep syntax'
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Kong, Leilei, Zhongyuan Han, Yong Han, and Haoliang Qi. "A Deep Paraphrase Identification Model Interacting Semantics with Syntax." Complexity 2020 (October 30, 2020): 1–14. http://dx.doi.org/10.1155/2020/9757032.
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Paraphrase identification is central to many natural language applications. Based on the insight that a successful paraphrase identification model needs to adequately capture the semantics of the language objects as well as their interactions, we present a deep paraphrase identification model interacting semantics with syntax (DPIM-ISS) for paraphrase identification. DPIM-ISS introduces the linguistic features manifested in syntactic features to produce more explicit structures and encodes the semantic representation of sentence on different syntactic structures by means of interacting semantics with syntax. Then, DPIM-ISS learns the paraphrase pattern from this representation interacting the semantics with syntax by exploiting a convolutional neural network with convolution-pooling structure. Experiments are conducted on the corpus of Microsoft Research Paraphrase (MSRP), PAN 2010 corpus, and PAN 2012 corpus for paraphrase plagiarism detection. The experimental results demonstrate that DPIM-ISS outperforms the classical word-matching approaches, the syntax-similarity approaches, the convolution neural network-based models, and some deep paraphrase identification models.
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Wu, Xianchao, Takuya Matsuzaki, and Jun’ichi Tsujii. "Improve syntax-based translation using deep syntactic structures." Machine Translation 24, no. 2 (June 2010): 141–57. http://dx.doi.org/10.1007/s10590-010-9081-6.
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Zhang, Zhining, Liang Wan, Kun Chu, Shusheng Li, Haodong Wei, and Lu Tang. "JACLNet:Application of adaptive code length network in JavaScript malicious code detection." PLOS ONE 17, no. 12 (December 14, 2022): e0277891. http://dx.doi.org/10.1371/journal.pone.0277891.
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Currently, JavaScript malicious code detection methods are becoming more and more effective. Still, the existing methods based on deep learning are poor at detecting too long or too short JavaScript code. Based on this, this paper proposes an adaptive code length deep learning network JACLNet, composed of convolutional block RDCNet, BiLSTM and Transfrom, to capture the association features of the variable distance between codes. Firstly, an abstract syntax tree recombination algorithm is designed to provide rich syntax information for feature extraction. Secondly, a deep residual convolution block network (RDCNet) is designed to capture short-distance association features between codes. Finally, this paper proposes a JACLNet network for JavaScript malicious code detection. To verify that the model presented in this paper can effectively detect variable JavaScript code, we divide the datasets used in this paper into long text dataset DB_Long; short text dataset DB_Short, original dataset DB_Or and enhanced dataset DB_Re. In DB_Long, our method’s F1 − score is 98.87%, higher than that of JSContana by 2.52%. In DB_Short, our method’s F1-score is 97.32%, higher than that of JSContana by 7.79%. To verify that the abstract syntax tree recombination algorithm proposed in this paper can provide rich syntax information for subsequent models, we conduct comparative experiments on DB_Or and DB_Re. In DPCNN+BiLSTM, F1-score with abstract syntax tree recombination increased by 1.72%, and in JSContana, F1-score with abstract syntax tree recombination increased by 1.50%. F1-score with abstract syntax tree recombination in JACNet improved by 1.00% otherwise unused.
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Ding, Jiaman, Weikang Fu, and Lianyin Jia. "Deep Forest and Pruned Syntax Tree-Based Classification Method for Java Code Vulnerability." Mathematics 11, no. 2 (January 15, 2023): 461. http://dx.doi.org/10.3390/math11020461.
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The rapid development of J2EE (Java 2 Platform Enterprise Edition) has brought unprecedented severe challenges to vulnerability mining. The current abstract syntax tree-based source code vulnerability classification method does not eliminate irrelevant nodes when processing the abstract syntax tree, resulting in a long training time and overfitting problems. Another problem is that different code structures will be translated to the same sequence of tree nodes when processing abstract syntax trees using depth-first traversal, so in this process, the depth-first algorithm will lead to the loss of semantic structure information which will reduce the accuracy of the model. Aiming at these two problems, we propose a deep forest and pruned syntax tree-based classification method (PSTDF) for Java code vulnerability. First, the breadth-first traversal of the abstract syntax tree obtains the sequence of statement trees, next, pruning statement trees removes irrelevant nodes, then we use a depth-first based encoder to obtain the vector, and finally, we use deep forest as the classifier to get classification results. Experiments on publicly accessible vulnerability datasets show that PSTDF can reduce the loss of semantic structure information and effectively remove the impact of redundant information.
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Gupta, Vikram, Haoyue Shi, Kevin Gimpel, and Mrinmaya Sachan. "Deep Clustering of Text Representations for Supervision-Free Probing of Syntax." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 10720–28. http://dx.doi.org/10.1609/aaai.v36i10.21317.
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We explore deep clustering of multilingual text representations for unsupervised model interpretation and induction of syntax. As these representations are high-dimensional, out-of-the-box methods like K-means do not work well. Thus, our approach jointly transforms the representations into a lower-dimensional cluster-friendly space and clusters them. We consider two notions of syntax: Part of Speech Induction (POSI) and Constituency Labelling (CoLab) in this work. Interestingly, we find that Multilingual BERT (mBERT) contains surprising amount of syntactic knowledge of English; possibly even as much as English BERT (E-BERT). Our model can be used as a supervision-free probe which is arguably a less-biased way of probing. We find that unsupervised probes show benefits from higher layers as compared to supervised probes. We further note that our unsupervised probe utilizes E-BERT and mBERT representations differently, especially for POSI. We validate the efficacy of our probe by demonstrating its capabilities as a unsupervised syntax induction technique. Our probe works well for both syntactic formalisms by simply adapting the input representations. We report competitive performance of our probe on 45-tag English POSI, state-of-the-art performance on 12-tag POSI across 10 languages, and competitive results on CoLab. We also perform zero-shot syntax induction on resource impoverished languages and report strong results.
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Khasanah, Noor. "Transformational Linguistics and the Implication Towards Second Language Learning." Register Journal 3, no. 1 (July 1, 2016): 23. http://dx.doi.org/10.18326/rgt.v3i1.23-36.
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The essence of Chomsky’s approach to language is the claim that there are linguistic universals in domain of syntax. He felt confident to show that syntax can be defined for any given language. For Chomsky, the nature of such mental representations is largely innate, so if a grammatical theory has explanatory adequacy it must be able to explain the various grammatical nuances of the languages of the world as relatively minor variations in the universal pattern of human language. In teaching English as L2, therefore knowing syntax and grammar of the language is important. Transformational Generative Grammar gives adequate elaboration in understanding them. Thus, the learners are expected to be able to avoid such ambiguity in interpreting the deep structure of a sentence since ambiguity will lead other people as the listeners or hearers of the speakers to misinterpret either consciously or unconsciously. Keywords: Surface Structure; Deep Structure; Constituent; Transformation
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Liang, Hongliang, Lu Sun, Meilin Wang, and Yuxing Yang. "Deep Learning With Customized Abstract Syntax Tree for Bug Localization." IEEE Access 7 (2019): 116309–20. http://dx.doi.org/10.1109/access.2019.2936948.
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Chlipala, Adam. "Skipping the binder bureaucracy with mixed embeddings in a semantics course (functional pearl)." Proceedings of the ACM on Programming Languages 5, ICFP (August 22, 2021): 1–28. http://dx.doi.org/10.1145/3473599.
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Rigorous reasoning about programs calls for some amount of bureaucracy in managing details like variable binding, but, in guiding students through big ideas in semantics, we might hope to minimize the overhead. We describe our experiment introducing a range of such ideas, using the Coq proof assistant, without any explicit representation of variables, instead using a higher-order syntax encoding that we dub "mixed embedding": it is neither the fully explicit syntax of deep embeddings nor the syntax-free programming of shallow embeddings. Marquee examples include different takes on concurrency reasoning, including in the traditions of model checking (partial-order reduction), program logics (concurrent separation logic), and type checking (session types) -- all presented without any side conditions on variables.
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Gupta, Rahul, Aditya Kanade, and Shirish Shevade. "Deep Reinforcement Learning for Syntactic Error Repair in Student Programs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 930–37. http://dx.doi.org/10.1609/aaai.v33i01.3301930.
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Novice programmers often struggle with the formal syntax of programming languages. In the traditional classroom setting, they can make progress with the help of real time feedback from their instructors which is often impossible to get in the massive open online course (MOOC) setting. Syntactic error repair techniques have huge potential to assist them at scale. Towards this, we design a novel programming language correction framework amenable to reinforcement learning. The framework allows an agent to mimic human actions for text navigation and editing. We demonstrate that the agent can be trained through self-exploration directly from the raw input, that is, program text itself, without either supervision or any prior knowledge of the formal syntax of the programming language. We evaluate our technique on a publicly available dataset containing 6975 erroneous C programs with typographic errors, written by students during an introductory programming course. Our technique fixes 1699 (24.4%) programs completely and 1310 (18.8%) program partially, outperforming DeepFix, a state-of-the-art syntactic error repair technique, which uses a fully supervised neural machine translation approach.
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Amini, Afra, Tiago Pimentel, Clara Meister, and Ryan Cotterell. "Naturalistic Causal Probing for Morpho-Syntax." Transactions of the Association for Computational Linguistics 11 (2023): 384–403. http://dx.doi.org/10.1162/tacl_a_00554.
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Abstract Probing has become a go-to methodology for interpreting and analyzing deep neural models in natural language processing. However, there is still a lack of understanding of the limitations and weaknesses of various types of probes. In this work, we suggest a strategy for input-level intervention on naturalistic sentences. Using our approach, we intervene on the morpho-syntactic features of a sentence, while keeping the rest of the sentence unchanged. Such an intervention allows us to causally probe pre-trained models. We apply our naturalistic causal probing framework to analyze the effects of grammatical gender and number on contextualized representations extracted from three pre-trained models in Spanish, the multilingual versions of BERT, RoBERTa, and GPT-2. Our experiments suggest that naturalistic interventions lead to stable estimates of the causal effects of various linguistic properties. Moreover, our experiments demonstrate the importance of naturalistic causal probing when analyzing pre-trained models. https://github.com/rycolab/naturalistic-causal-probing
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Abdurahmani, Edlira Troplini. "The First Attempts for the Syntax of the Regional Variety of Chameria." European Journal of Interdisciplinary Studies 4, no. 2 (July 24, 2018): 174. http://dx.doi.org/10.26417/ejis.v4i2.p174-180.
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This paper is part of the chainwork that deals the syntax study of the dialect of the province of Chameria, a prominent and conservative language of the south of Albania. This study is the second in this regard. Initially, we were dealing with the intonation of Chams and mainly with its syntactic value (Troplini 2016). For the syntax of the of the regional variety of Chameria, there is no previous effort. In this paper, as a basic literature, we have only used our previous works for the Cham in the sociolinguistic framework, where we were dealing with phonetic, morphological, lexical, historical linguistics etc. Our first effort also underlines the first difficulties of this syntactic treatment, as well as the objectives that need to be fulfilled in the perspective, through a deep scientific monograph that will conclude a long-term gap in the study of the cham variety in terms of syntax. The method we used is the inductive one. The work has been based on a qualitative research basis. We are based on a series of direct interviews with elderly speakers of this province who were born mainly in Chameria and subjected to ethnic cleansing. This generation well preserves the most southern and extreme Albanian talk. In this study, we intend to highlight some of the most important phenomena of the Cham variety which will then serve for a deep work in terms of syntax.
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Xia, Qingrong, Zhenghua Li, Min Zhang, Meishan Zhang, Guohong Fu, Rui Wang, and Luo Si. "Syntax-Aware Neural Semantic Role Labeling." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7305–13. http://dx.doi.org/10.1609/aaai.v33i01.33017305.
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Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP. Motivated by the close correlation between syntactic and semantic structures, traditional discrete-feature-based SRL approaches make heavy use of syntactic features. In contrast, deep-neural-network-based approaches usually encode the input sentence as a word sequence without considering the syntactic structures. In this work, we investigate several previous approaches for encoding syntactic trees, and make a thorough study on whether extra syntax-aware representations are beneficial for neural SRL models. Experiments on the benchmark CoNLL-2005 dataset show that syntax-aware SRL approaches can effectively improve performance over a strong baseline with external word representations from ELMo. With the extra syntax-aware representations, our approaches achieve new state-of-the-art 85.6 F1 (single model) and 86.6 F1 (ensemble) on the test data, outperforming the corresponding strong baselines with ELMo by 0.8 and 1.0, respectively. Detailed error analysis are conducted to gain more insights on the investigated approaches.
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Murni, Tri, Robert Sibarani, Eddy Setia, and Gustianigsih Gustianingsihh. "MOVEMENT TRANSFORMATION IN GAYO SYNTAX." Language Literacy: Journal of Linguistics, Literature, and Language Teaching 3, no. 1 (July 5, 2019): 21–32. http://dx.doi.org/10.30743/ll.v3i1.906.
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The purpose of this study is to present syntactic descriptions of how movement transformational rules apply in Gayo syntax and to examine the status of movement transformational rules in Gayo language (henceforth GL) in the theoretical framework of Transformational Linguistics (TL) proposed by Chomsky (1965, 1981) and Suhadi (2018). In this theory there are three kinds of syntactic rules: Movement Rule, Deletion Rule and Substitution Rule. The discussion focuses on Movement Transformational Rules in GL. Transformation is the inter-related process between the deep structure and the surface structure of a sentence by the application of one or more transformational rules. The method used in this study was descriptive qualitative approach as noted by Martin (2004). Descriptive research is to portray accurately the characteristics of a particular situation or group or individual with or without special initial hypotheses about the nature of these characteristics. Thus, descriptive research design was applied to give a detail description of a certain case accurately. The data were analyzed from two angles: the application and the status of movement rules in GL, which can be compulsory, optional, and restricted. The data of this research derived from some sentences in the folklore story written in GL and the invention of the writer herself as the native speaker of the language. The finding shows that all the twelve kinds of movement transformational rules proposed by Suhadi (2018) are relevant to apply in GL. After the application of movement rules, the main finding is on the status of movement transformational rules in GL in which it is found that four movement rules are compulsory, eight are optional and there is no restricted rule in the language.
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Kondratieva, N. V., and A. F. Utkina. "TO THE HISTORY OF STUDYING THE UDMURT SYNTAX." Bulletin of Udmurt University. Series History and Philology 31, no. 5 (October 28, 2021): 925–38. http://dx.doi.org/10.35634/2412-9534-2021-31-5-925-938.
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The article describes and systematizes scientific research devoted to the study of the Udmurt syntax in synchrony and diachrony. Based on the peculiarities of the methodological principles and attitudes’ development, it is proposed to distinguish three stages in the history of studying the syntax of the Udmurt language. The first stage (from the second half of the 18th century to 1851) is characterized by the absence of a systematic approach to the study of the syntactic structure of the language, insufficient delimitation of the spheres of morphology and syntax. The second stage (from 1851 to 1939) is characterized by a closer attention to the syntactic nature of linguistic phenomena, as well as to the development of terminology in the Udmurt language. However, a deep scientific understanding of the Udmurt syntax began only at the third stage (from 1939 to the present), when the structural-semantic principle dominates in the study of syntactic constructions, providing for equal attention to the structure (construction) of a syntactic unit and the meaning (semantics) that it is embodied in it.
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Dresher, B. Elan, and Norbert Hornstein. "Language and the deep unconscious mind: Aspectualities of the theory of syntax." Behavioral and Brain Sciences 13, no. 4 (December 1990): 602–3. http://dx.doi.org/10.1017/s0140525x00080377.
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Zhou, YiTao. "Natural Language Processing with Improved Deep Learning Neural Networks." Scientific Programming 2022 (January 7, 2022): 1–8. http://dx.doi.org/10.1155/2022/6028693.
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As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. This paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. This paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier. By analyzing the model, we have made meticulous parameters of the model to improve its performance. This paper proposes a dependent syntactic analysis model based on a long-term memory neural network. This model is based on the feed-forward neural network model described above and will be used as a feature extractor. After the feature extractor is pretrained, we use a long short-term memory neural network as a classifier of the transfer action, and the characteristics extracted by the syntactic analyzer as its input to train a recursive neural network classifier optimized by sentences. The classifier can not only classify the current pattern feature but also multirich information such as analysis of state history. Therefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. The experimental results show that the model has achieved greater performance improvement than baseline methods.
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Hamed Shoay Saleh Al-Mogarry. "Syntax Semantics Interface and Translation." Albaydha University Journal 4, no. 1 (April 19, 2022): 144–55. http://dx.doi.org/10.56807/buj.v4i1.239.
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تهدف هذه الدراسة الى دراسة نقطة الاتصال والتداخل بين علم النحو وعلم الدلالة في اللغتين العربية والانجليزية وبالأخص دراسة تطبيقات منهج النحو التوليدي التحويلي لتشومسكي ومنهج الوظائف الموضوعية لفيل مور في بعض الجمل الانجليزية كنص أصل وترجمتها الى اللغة العربية كنص هدف من خلال التحليل النحوي والدلالي. اوضحت الدراسة وجود اتصال وتداخل بين علم النحو وعلم الدلالة في كلتا اللغتين. واثبتت الدراسة ايضا امكانية استخدام منهج ثنائي في التحليل اللغوي ودراسة العلاقة بين أي لغتين. كما اشارت الدراسة بان التحليل المستند الى علم النحو والدلالة يمكن ان يشكل عاملا مساعدا للمترجم من الانجليزية الى العربية والعكس.
Abstract
This study attempts to investigate the interface between syntax and semantics in English and Arabic. It is to study the potential implications of Transformational Generative Grammar (TGG) and thematic roles in translation from a linguistic point of view. It presents a theoretical background of TGG and thematic roles as the basis of the analysis applied on certain English sentences from three different texts along with their Arabic translations. Those sentences and their Arabic translations are analyzed syntactically according to Chomsky's TGG (1965) and semantically to Fillmore's thematic roles (1977). The results of the study show that there is some kind of interface within these languages in terms of syntax and semantics. This emphasizes the idea of linguistic universals by Chomsky. The application of co-analysis showed that those syntactic and semantic aspects can be relatively similar in English and Arabic particularly in terms of the type of the NPs in the deep and surface structures and semantic roles.
The study concludes that translation is a practical field where the feasibility and utility of linguistic theories and ideas can be tested, and integration and application of more than one approach in translation e.g. TGG analysis and semantic roles can help in producing more appropriate translations. However, it should be stated that syntax semantics interface and its overlapping can't be solely used in translation studies.
Key Words: Syntax- Semantics- Interface- Translation- analysis- implications.
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Kriswantara, Bambang, Kurniawati Kurniawati, and Hilman F. Pardede. "Prediksi Harga Mobil Bekas dengan Machine Learning." Syntax Literate ; Jurnal Ilmiah Indonesia 6, no. 5 (May 21, 2021): 2100. http://dx.doi.org/10.36418/syntax-literate.v6i5.2716.
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Harga mobil bekas dipengaruhi oleh beberapa faktor yang berkaitan dengan mobil itu sendiri, seperti jenis mobil, model, edisi, tahun produksi, transmisi, bahan bakar, kapasitas mesin, dan jarak tempuh. Harganya juga fluktuatif dan persaingan yang tinggi antar penjual mobil bekas, dibutuhkan alat untuk memprediksi harga mobil bekas secara akurat dan cepat. Tujuan dari penelitian ini untuk membantu show room mobil bekas dalam memprediski harga secara cepat dengan data history menggunakan metode Deep Neural Network (DNN) dengan tiga lapisan tersembunyi. Dalam Penelitian ini dilakukan dua pendekatan utama penelitian, yaitu pendekatan kualitatif dan pendekatan kuantitatif. Hasil penelitian kami menghasilkan MAE=501232, R2=0.88 yang lebih baik dari penelitian sebelumnya dengan metode Random Forest menghasilkan MAE=521947, R2=0.82. Sehingga Metode DNN akan meningkatkan akurasi prediski yang lebih baik, meskipun tidak naik secara signifikan mengacu pada hasil penelitian.
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Vaghasia, Rishil. "An Improvised Approach of Deep Learning Neural Networks in NLP Applications." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 1599–603. http://dx.doi.org/10.22214/ijraset.2023.48884.
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Abstract: In recent years, natural language processing (NLP) has drawn a lot of interest for its ability to computationally represent and analyze human language. Its uses have expanded to include machine translation, email spam detection, information extraction, summarization, medical diagnosis, and question answering, among other areas. The purpose of this research is to investigate how deep learning and neural networks are used to analyze the syntax of natural language. This research first investigates a feed-forward neural network-based classifier for a transfer-based dependent syntax analyzer. This study presents a long-term memory neural network-based dependent syntactic analysis paradigm. This model, which will serve as a feature extractor, is based on the feed-forward neural network model mentioned before. After the feature extractor is learned, we train a recursive neural network classifier that is optimized by sentences using a long short-term memory neural network as a classifier of the transfer action and the characteristics retrieved by the syntactic analyzer as its input. Syntactic analysis replaces the method of modeling independent analysis with one that models the analysis of the entire sentence as a whole. The experimental findings demonstrate that the model has improved its performance more than the benchmark techniques.
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Pavlick, Ellie. "Semantic Structure in Deep Learning." Annual Review of Linguistics 8, no. 1 (January 14, 2022): 447–71. http://dx.doi.org/10.1146/annurev-linguistics-031120-122924.
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Deep learning has recently come to dominate computational linguistics, leading to claims of human-level performance in a range of language processing tasks. Like much previous computational work, deep learning–based linguistic representations adhere to the distributional meaning-in-use hypothesis, deriving semantic representations from word co-occurrence statistics. However, current deep learning methods entail fundamentally new models of lexical and compositional meaning that are ripe for theoretical analysis. Whereas traditional distributional semantics models take a bottom-up approach in which sentence meaning is characterized by explicit composition functions applied to word meanings, new approaches take a top-down approach in which sentence representations are treated as primary and representations of words and syntax are viewed as emergent. This article summarizes our current understanding of how well such representations capture lexical semantics, world knowledge, and composition. The goal is to foster increased collaboration on testing the implications of such representations as general-purpose models of semantics.
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Arsenius, Arsenius, and Dety Mulyanti. "Pengaruh Promosi terhadap Peningkatan Hasil Penjualan Produk Jasa Perbankan pada Pt. Bank Muamalat Indonesia Tbk." Syntax Idea 5, no. 4 (April 24, 2023): 459. http://dx.doi.org/10.46799/syntax-idea.v5i4.2208.
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The Bontolempangan community, Buki District, Selayar Islands Regency is a traditional village that still maintains the traditional culture of their ancestors. The marriage system that still exists in the era of globalization is adat attaralu. The purpose of this study is to determine the symbolic meanings contained in the Attaralu custom carried out in Bontolempangan Village, Buki District, Selayar Islands Regency. This research uses qualitative methods through a case study approach with descriptive analysis including collecting data, presenting, displaying data, and drawing conclusions. Data collection was carried out using observation, interview, and documentation techniques. The results of this study show that there are three main processes in the implementation of attaralu customs, namely a'pissalingi, ambaung patingroang, and attaralu, of these three processes have a deep philosophical meaning for the people of Bontolemapangan Village, Buki District, Selayar Islands Regency.
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Yuliana, Yuliana, Muhamad Ramli, and Radjab Mansur. "The Symbolic Meaning of Attaralu Custom in the Marriage System of the People of Bontolempangan Village, Bukit District, Selayar Islands Regency." Syntax Idea 5, no. 4 (April 23, 2023): 444. http://dx.doi.org/10.46799/syntax-idea.v5i4.2191.
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The Bontolemngan community, Buki District, Selayar Islands Regency is a traditional village that still maintains their traditional ancestral culture. The marriage system that still exists in the era of globalization is adat attaralu. the purpose of this study is to find out the symbolic meanings contained in the attaralu custom which is carried out in Bontolemngan Village, Buki District, Selayar Islands Regency. This study uses qualitative methods through a case study approach with descriptive analysis including collecting presentation data, displaying data and drawing conclusions. Data collection was carried out using observation, interview, and documentation techniques. The results of this study indicate that there are three main processes in implementing the attaralu custom, namely a'pissalingi, ambaung patingroang, and attaralu, these three processes have deep philosophical meaning for the people of Bontolemapangan Village, Buki District, Selayar Islands Regency.
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Harris, Randy Allen. "The origin and developmemt of generative semantics." Historiographia Linguistica 20, no. 2-3 (January 1, 1993): 399–440. http://dx.doi.org/10.1075/hl.20.2-3.07har.
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Summary Against the background of the controversial and polarized work of Frederick Newmeyer and Robin Tolmach Lakoff, this paper chronicles the early development of generative semantics, an internal movement within the transformational model of Chomsky’s Aspects of the Theory of Syntax. The first suggestions toward the movement, whose cornerstone was the obliteration of the syntax-semantics boundary, were by George Lakoff in 1963. But it was the work conducted under the informal banner of “Abstract Syntax” by Paul Postal that began the serious investigations leading to such an obliteration. Lakoff was an active participant in that research, as were Robin Tolmach Lakoff, John Robert (“Háj”) Ross and James D. McCawley. Through their combined efforts, particularly those of McCawley on semantic primitives and lexical insertion, generative semantics took shape in 1967: positing a universal base, importing notions from predicate calculus, decomposing lexical structure, and, most contentiously, rejecting the central element of the Aspects model, deep structure.
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Poiret, Rafaël, Simon Mille, and Haitao Liu. "Paraphrase and parallel treebank for the comparison of French and Chinese syntax." Languages in Contrast 21, no. 2 (April 20, 2021): 298–322. http://dx.doi.org/10.1075/lic.20002.poi.
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Abstract This paper proposes to study the contrastive syntax of French and Chinese through the lens of syntactic mismatches, and by making use of parallel treebanks. A syntactic mismatch is the non-similarity between the syntactic structures of one linguistic unit and its translation. Syntactic mismatches are formalized using the notion of paraphrase from the Meaning-Text Theory, which allows for capturing mismatches at different levels of the linguistic description (e.g. Semantic, Deep-Syntactic, and Surface-Syntactic). In this paper, we report in details on the types of paraphrases found in the seed corpus used, demonstrating that the Deep-Syntactic paraphrases constitute the best starting point for our study. Then, we show how, starting from the seed corpus, we semi-automatically constructed a multi-layer parallel treebank with the alignment and annotation of paraphrases.
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GIOVANNETTI, TANIA, MARY W. HOPKINS, JACLYN CRAWFORD, BRIANNE MAGOUIRK BETTCHER, KARA S. SCHMIDT, and DAVID J. LIBON. "Syntactic comprehension deficits are associated with MRI white matter alterations in dementia." Journal of the International Neuropsychological Society 14, no. 4 (June 25, 2008): 542–51. http://dx.doi.org/10.1017/s1355617708080715.
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Comprehension difficulties associated with periventricular and deep white matter alterations (WMA) in mild dementia were investigated using portions of the Boston Diagnostic Aphasia Examination (BDAE) Complex Ideation subtest and Syntax subtests. Mild dementia participants were grouped according to the extent of their WMA as observed on magnetic resonance imaging (mild WMA n = 45 vs. moderate to severe WMA n = 52). Correlation and regression analyses also were performed to examine the link between WMA and comprehension abilities, as well as the link between comprehension abilities and neuropsychological measures of executive functioning, language, episodic memory, and overall dementia severity. Results showed that the WMA groups differed on the BDAE-Syntax subtests, with the severe WMA group demonstrating more impairment. Correlation and regression analyses including the entire sample also demonstrated that the extent of WMA was significantly linked to Syntax test scores but not Complex Ideation scores. Regression analyses including neuropsychological measures showed that the BDAE-Complex Ideation score was marginally predicted by only overall dementia severity, whereas the BDAE-Syntax scores were significantly predicted by independent measures of working memory/executive functioning. In conclusion, greater subcortical WMA and executive deficits are associated with greater difficulties in syntactic comprehension in individuals with mild dementia. (JINS, 2008, 14, 542–551.)
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Brusasco, Paolo. "Theory and practice in the study of Mesopotamian domestic space." Antiquity 78, no. 299 (March 2004): 142–57. http://dx.doi.org/10.1017/s0003598x00092991.
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This study compares the results of space syntax analysis of houses in Babylonian Ur with similar analyses on modern households in Baghdad and among the Ashanti. The social organisations identified were then compared with the written evidence for Ur surviving on site in cuneiform tablets. This opportunity to examine spatial, ethnographic and documentary evidence together offers a deep reading of Ur society.
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Ware, Stephen G., and Cory Siler. "Sabre: A Narrative Planner Supporting Intention and Deep Theory of Mind." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 17, no. 1 (October 4, 2021): 99–106. http://dx.doi.org/10.1609/aiide.v17i1.18896.
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Sabre is a narrative planner—a centralized, omniscient decision maker that solves a multi-agent storytelling problem. The planner has an author goal it must achieve, but every action taken by an agent must make sense according to that agent's individual intentions and limited, possibly wrong beliefs. This paper describes the implementation of Sabre, which supports a rich action syntax and imposes no arbitrary limit on the depth of theory of mind. We present a search procedure for generating plans that achieve the author goals while ensuring all agent actions are explained, and we report the system's performance on several narrative planning benchmark problems.
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Wong, Emily S., Dawei Zheng, Siew Z. Tan, Neil I. Bower, Victoria Garside, Gilles Vanwalleghem, Federico Gaiti, et al. "Deep conservation of the enhancer regulatory code in animals." Science 370, no. 6517 (November 5, 2020): eaax8137. http://dx.doi.org/10.1126/science.aax8137.
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Interactions of transcription factors (TFs) with DNA regulatory sequences, known as enhancers, specify cell identity during animal development. Unlike TFs, the origin and evolution of enhancers has been difficult to trace. We drove zebrafish and mouse developmental transcription using enhancers from an evolutionarily distant marine sponge. Some of these sponge enhancers are located in highly conserved microsyntenic regions, including an Islet enhancer in the Islet-Scaper region. We found that Islet enhancers in humans and mice share a suite of TF binding motifs with sponges, and that they drive gene expression patterns similar to those of sponge and endogenous Islet enhancers in zebrafish. Our results suggest the existence of an ancient and conserved, yet flexible, genomic regulatory syntax that has been repeatedly co-opted into cell type–specific gene regulatory networks across the animal kingdom.
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Pang, Xiongwen, Yanqiang Zhou, Pengcheng Li, Weiwei Lin, Wentai Wu, and James Z. Wang. "A novel syntax-aware automatic graphics code generation with attention-based deep neural network." Journal of Network and Computer Applications 161 (July 2020): 102636. http://dx.doi.org/10.1016/j.jnca.2020.102636.
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Ullah, Farhan, Sohail Jabbar, and Fadi Al-Turjman. "Programmers' de-anonymization using a hybrid approach of abstract syntax tree and deep learning." Technological Forecasting and Social Change 159 (October 2020): 120186. http://dx.doi.org/10.1016/j.techfore.2020.120186.
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Widiyanto, Iwan Firman. "The Value Reality of Peace from The Cosmogonic Myth of The Srumbung Gunung Hamlet." JURNAL SYNTAX IMPERATIF : Jurnal Ilmu Sosial dan Pendidikan 3, no. 1 (March 21, 2022): 23. http://dx.doi.org/10.36418/syntax-imperatif.v3i1.145.
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This research is based on the idea that myth as a form of culture projects the reality of the values that are lived by the community, talking about collective ideals about the form of society that is dreamed of together. In addition, myth also shows the tendency of people to overcome conflict. This study uses an ethnographic qualitative research method. Thus, the data were taken from direct observations and from interviews with community leaders or elders of the Srumbung Gunung hamlet to find out various versions of the myth of the origin of the Srumbung Gunung hamlet. These data are then analyzed using an ideational cultural framework, exploring the cosmogonic myth of Dusun Srumbung Gunung. Seeing the layers of values that wrap around myth, specifically to understand the values that support peace while at the same time seeing the tendency of community patterns to resolve conflicts. Furthermore, the results of this study show that the reality of the value of peace in myth is prioritizing dialogue or deliberation, cooperation and kinship, respect for nature, respect for those who are different and living an attitude of like to work hard, dare to sacrifice in seeking a prosperous life. This myth also shows the tendency of community patterns in dealing with conflict. There is a tendency for some people to take aggressive actions, but on the other hand there are also those who tend to be more preventive, restrictive and self-defense rather than attack. In the end, a deep understanding of culture is important for peace-building efforts to be more effective.
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Nurrofiq, Inan. "Implementasi Model Pembelajaran Cooperative Learning Type Stad dengan Pendekatan Kontekstual untuk Meningkatkan Kemampuan Siswa dalam Memahami Trigonometri." Syntax Idea 3, no. 11 (November 21, 2021): 2413. http://dx.doi.org/10.36418/syntax-idea.v3i11.1577.
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SMA Negeri 1 Sukapura in class X MIA has learning problems in Mathematics, namely Trigonometry. In Class X MIA SMA Negeri 1 Sukapura, many students do not understand the material presented by the Mathematics teacher. The low understanding of students towards Trigonometry is caused by students having difficulty understanding the material presented by the teacher., Students are not actively involved in learning and it is difficult to gain deep understanding. This results in students' ability to understand trigonometry. Therefore, this study takes the title "Implementation of STAD type Cooperative Learning Learning Model with a Contextual Approach to Improve Students' Ability to Understand Trigonometry in Class X MIA SMA Negeri 1 Sukapura Academic Year 2017/2018" Learning Model Cooperative Learning type STAD through a Contextual Approach is a teaching method that emphasizes activities and interactions between students to motivate each other and help each other in mastering the subject matter in order to achieve maximum performance by presenting real-world situations into the classroom. Comparison of research results, namely in the pre-cycle the number of students who finished studying was only 19 students or 76%, then in cycle 1 it increased to 20 students or 80%, and increased in cycle 2 to 22 students or 88%. From the comparison of the results of the study, It can be concluded that, with the concept of 4X meetings, the STAD Cooperative Learning model learning with a Contextual Approach has a positive impact on the process of increasing students' ability to understand Trigonometry
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Meng, Yao. "An Intelligent Code Search Approach Using Hybrid Encoders." Wireless Communications and Mobile Computing 2021 (July 1, 2021): 1–16. http://dx.doi.org/10.1155/2021/9990988.
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The intelligent code search with natural language queries has become an important researching area in software engineering. In this paper, we propose a novel deep learning framework At-CodeSM for source code search. The powerful code encoder in At-CodeSM, which is implemented with an abstract syntax tree parsing algorithm (Tree-LSTM) and token-level encoders, maintains both the lexical and structural features of source code in the process of code vectorizing. Both the representative and discriminative models are implemented with deep neural networks. Our experiments on the CodeSearchNet dataset show that At-CodeSM yields better performance in the task of intelligent code searching than previous approaches.
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Afrida, Nina, and Khairurrahmi. "STUDENTS’ ABILITY IN TRANSFORMING A SENTENCE: DEEP AND SURFACE STRUCTURE." JL3T ( Journal of Linguistics Literature and Language Teaching) 2, no. 2 (January 25, 2017): 53–66. http://dx.doi.org/10.32505/jl3t.v2i2.14.
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The present study aims at exploring students’ difficulties in determining deep and surface structure in sentences. The study used quantitative descriptive as a research design. Test and questionnaire were employed as data collection technique. The population of the study is all seventh semester students of English Education Department, since they have studied syntax in the previous semesters. 50 students were sampled in this study. The study reveals that most of students cannot transform deep structure into surface structure. They only can identify the surface structure of simple sentences. The study also shows that English Education Department students find much difficulty in determining the labeling of the words in surface structure, for they cannot classify the class of wordos in surface structure. The study suggests that the ability to differentiate word categories be essential in transforming the surface structure generated through deep structure.
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Lin, Huei-Ling. "On the Syntax of Null Clausal Complements in Taiwan Southern Min." Studies in Chinese Linguistics 43, no. 1 (June 1, 2022): 21–37. http://dx.doi.org/10.2478/scl-2022-0002.
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Abstract This paper investigates the less discussed null argument – the null clausal complement in Taiwan Southern Min (TSM). The discussion issues include the derivation, status, and replacement of null clausal complements in TSM. This paper applies four tests to prove that the null clausal complement in TSM is a type of deep anaphora, which has no internal structure and is not derived through deletion. Moreover, possessing features such as not being A-bound, and possibly being but not required to be A-bar-bound, the null clausal complement in TSM is argued to have the status of a null epithet. As null clausal complements are not allowed with all kinds of verbs, in some cases where clausal complements cannot be null, an obligatory pro-S an-ne ‘so’ is then required.
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Dr. Diptiranjan Maharana. "Communicative Language Teaching: A Study on English Syntax and Phonology." Creative Launcher 4, no. 5 (December 31, 2019): 8–14. http://dx.doi.org/10.53032/tcl.2019.4.5.02.
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Language is the most widely used tool that Human beings use for interacting with each other to exchange their ideas, information, feelings and sentiments. It acts as a repository of wisdom, a propeller for the advancement of knowledge and telescope to view the vision of the future in this fast changing world. The English language has played a vital role. It is the most influential language the world has ever witnessed. Super-power politics has not been able to set boundaries for it. It is the closest thing to a lingua franca around the world. English has penetrated deep into all business sectors and has established itself as a global passport for professional success. The teaching skills and strategies should be developed keeping pace with the current demands of the global market. More attention needs to be paid on the development of learners’ competence with focus on evolving a more effective and successful method. Language is the most important communicative tool of mankind, and English language has become a universal medium of communication in today’s era of liberalization and globalization.
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Colomina, María Pilar. "A distinctness approach to clitic combinations in Romance." Studies in Hispanic and Lusophone Linguistics 13, no. 2 (October 25, 2020): 277–308. http://dx.doi.org/10.1515/shll-2020-2031.
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AbstractThis paper analyses the combinatorial restrictions that operate in clitic clusters in certain Eastern Iberian varieties (Aragonese, Spanish, and Catalan). In particular, I focus on the combination of third person clitics. As it is well known, in some Romance varieties the combination of a third person accusative clitic and a third person dative clitic is banned (the so-called ∗le lo restriction, Bonet, Eulàlia. 1991. Morphology after syntax: Pronominal clitics in Romance. Cambridge: Massachusetts Institute of Technology Dissertation; Cuervo, María Cristina. 2013. Spanish clitic clusters: Three of a perfect pair. Borealis: An International Journal of Hispanic Linguistics 2. 191–220; Nevins, Andrew. 2007. The representation of third person and its consequences for person-case effects. Natural Language & Linguistic Theory 25(2). 273–313; Ordóñez, Francisco. 2002. Some clitic combinations in the syntax of Romance. Catalan Journal of Linguistics 1. 201–224, Ordóñez, Francisco. 2012. Clitics in Spanish. In José I. Hualde, Antxon Olarrea & Erin O’Rouke (eds.), The handbook of Spanish Linguistics, 423–453. Malden: Wiley-Blackwell; Perlmutter, David. 1971. Deep and surface structure constraints in syntax. New York: Holt, Rinehart & Winston). In order to license this troublesome combination, languages resort to different ‘repair strategies’ modifying the structure of one of the merged clitics. In the literature on clitic combinations, there have been two main proposals of analysis: morphological and syntactical. In this paper, I put forward an analysis based on the Distinctness Condition (Hiraiwa, Ken. 2010. The syntactic OCP. In Yukio Otsu (ed.), The proceedings of the 11th Tokyo Conference on Psycholinguistics, 35–56. Hituzi: Tokyo; Neeleman, Ad & Hans van de Koot. 2005. Syntactic haplology. In Martin Everaert & Henk van Riemsdijk (eds.), The Blackwell companion to syntax, 685–710. Wiley-Blackwell; Perlmutter, David. 1971. Deep and surface structure constraints in syntax. New York: Holt, Rinehart & Winston; Richards, Norvin. 2010. Uttering trees, vol. 56. Cambridge, MA: MIT Press; Van Riemsdijk, Henk. 1998. Categorial feature magnetism: The endocentricity and distribution of projections. The Journal of Comparative Germanic Linguistics 2(1). 1–48; Yip, Moira. 1998. Identity avoxidance in phonology and morphology. In Steven G. Lapointe, Diane K. Brentari & Patrick M. Farell (eds.), Mophology and its relation to phonology and syntax, 216–246. Stanford, CA: CSLI). Specifically, I argue that the restrictions that constraint clitic combinations are due to the impossibility to linearize two identical syntactic objects, such as <XP, XP> (Chomsky, Noam. 2013. Problems of projection. Lingua 130. 33–49; Chomsky, Noam. 2015. Problems of projection. In Elisa Di Domenico, Cornelia Hamann & Simona Matteini (eds.), Structures, strategies and beyond: Studies in honour of Adriana Belletti, 1–16. Amsterdam: John Benjamins; Moro, Andrea. 2000. Dynamic antisymmetry (No. 38). Cambridge, MA: MIT Press; Richards, Norvin. 2010. Uttering trees, vol. 56. Cambridge, MA: MIT Press). From this perspective, cross-linguistic variation is the result of different ‘repair strategies’ languages deploy to make <XP, XP> objects linearizable (Richards, Norvin. 2010. Uttering trees, vol. 56. Cambridge, MA: MIT Press).
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Chen, Jian, Jianpeng Chen, Xiangrong She, Jian Mao, and Gang Chen. "Deep Contrast Learning Approach for Address Semantic Matching." Applied Sciences 11, no. 16 (August 19, 2021): 7608. http://dx.doi.org/10.3390/app11167608.
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Address is a structured description used to identify a specific place or point of interest, and it provides an effective way to locate people or objects. The standardization of Chinese place name and address occupies an important position in the construction of a smart city. Traditional address specification technology often adopts methods based on text similarity or rule bases, which cannot handle complex, missing, and redundant address information well. This paper transforms the task of address standardization into calculating the similarity of address pairs, and proposes a contrast learning address matching model based on the attention-Bi-LSTM-CNN network (ABLC). First of all, ABLC use the Trie syntax tree algorithm to extract Chinese address elements. Next, based on the basic idea of contrast learning, a hybrid neural network is applied to learn the semantic information in the address. Finally, Manhattan distance is calculated as the similarity of the two addresses. Experiments on the self-constructed dataset with data augmentation demonstrate that the proposed model has better stability and performance compared with other baselines.
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Batanovic, Vuk, and Dragan Bojic. "Using part-of-speech tags as deep-syntax indicators in determining short-text semantic similarity." Computer Science and Information Systems 12, no. 1 (2015): 1–31. http://dx.doi.org/10.2298/csis131127082b.
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This paper presents POST STSS, a method of determining short-text semantic similarity in which part-of-speech tags are used as indicators of the deeper syntactic information usually extracted by more advanced tools like parsers and semantic role labelers. Our model employs a part-of-speech weighting scheme and is based on a statistical bag-of-words approach. It does not require either hand-crafted knowledge bases or advanced syntactic tools, which makes it easily applicable to languages with limited natural language processing resources. By using a paraphrase recognition test, we demonstrate that our system achieves a higher accuracy than all existing statistical similarity algorithms and solutions of a more structural kind.
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Velldal, Erik, Lilja Øvrelid, Jonathon Read, and Stephan Oepen. "Speculation and Negation: Rules, Rankers, and the Role of Syntax." Computational Linguistics 38, no. 2 (June 2012): 369–410. http://dx.doi.org/10.1162/coli_a_00126.
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This article explores a combination of deep and shallow approaches to the problem of resolving the scope of speculation and negation within a sentence, specifically in the domain of biomedical research literature. The first part of the article focuses on speculation. After first showing how speculation cues can be accurately identified using a very simple classifier informed only by local lexical context, we go on to explore two different syntactic approaches to resolving the in-sentence scopes of these cues. Whereas one uses manually crafted rules operating over dependency structures, the other automatically learns a discriminative ranking function over nodes in constituent trees. We provide an in-depth error analysis and discussion of various linguistic properties characterizing the problem, and show that although both approaches perform well in isolation, even better results can be obtained by combining them, yielding the best published results to date on the CoNLL-2010 Shared Task data. The last part of the article describes how our speculation system is ported to also resolve the scope of negation. With only modest modifications to the initial design, the system obtains state-of-the-art results on this task also.
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Dahl, Amanda C., Sarah E. Carlson, Maggie Renken, Kathryn S. McCarthy, and Erin Reynolds. "Materials Matter: An Exploration of Text Complexity and Its Effects on Middle School Readers' Comprehension Processing." Language, Speech, and Hearing Services in Schools 52, no. 2 (April 20, 2021): 702–16. http://dx.doi.org/10.1044/2021_lshss-20-00117.
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Purpose Complex features of science texts present idiosyncratic challenges for middle grade readers, especially in a post–Common Core educational world where students' learning is dependent on understanding informational text. The primary aim of this study was to explore how middle school readers process science texts and whether such comprehension processes differed due to features of complexity in two science texts. Method Thirty 7th grade students read two science texts with different profiles of text complexity in a think-aloud task. Think-aloud protocols were coded for six comprehension processes: connecting inferences, elaborative inferences, evaluative comments, metacognitive comments, and associations. We analyzed the quantity and type of comprehension processes generated across both texts in order to explore how features of text complexity contributed to the comprehension processes students produced while reading. Results Students made significantly more elaborative and connecting inferences when reading a text with deep cohesion, simple syntax, and concrete words, while students made more evaluative comments, paraphrases, and metacognitive comments when reading a text with referential cohesion, complex syntax, and abstract words. Conclusions The current study provides exploratory evidence for features of text complexity affecting the type of comprehension processes middle school readers generate while reading science texts. Accordingly, science classroom texts and materials can be evaluated for word, sentence, and passage features of text complexity in order to encourage deep level comprehension of middle school readers.
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Pardede, Hilman F., Purwoko Adhi, Vicky Zilvan, Ade Ramdan, and Dikdik Krisnandi. "Deep convolutional neural networks-based features for Indonesian large vocabulary speech recognition." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (June 1, 2023): 610. http://dx.doi.org/10.11591/ijai.v12.i2.pp610-617.
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There are great interests in developing speech recognition using deep learning technologies due to their capability to model the complexity of pronunciations, syntax, and language rules of speech data better than the traditional hidden Markov model (HMM) do. But, the availability of large amount of data is necessary for deep learning-based speech recognition to be effective. While this is not a problem for mainstream languages such as English or Chinese, this is not the case for non-mainstream languages such as Indonesian. To overcome this limitation, we present deep features based on convolutional neural networks (CNN) for Indonesian large vocabulary continuous speech recognition in this paper. The CNN is trained discriminatively which is different from usual deep learning implementations where the networks are trained generatively. Our evaluations show that the proposed method on Indonesian speech data achieves 7.26% and 9.01% error reduction rates over the state-of-the-art deep belief networks-deep neural networks (DBN-DNN) for large vocabulary continuous speech recognition (LVCSR), with Mel frequency cepstral coefficients (MFCC) and filterbank (FBANK) used as features, respectively. An error reduction rate of 6.13% is achieved compared to CNN-DNN with generative training.
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Maslova, Alexandra, Ricardo N. Ramirez, Ke Ma, Hugo Schmutz, Chendi Wang, Curtis Fox, Bernard Ng, Christophe Benoist, and Sara Mostafavi. "Deep learning of immune cell differentiation." Proceedings of the National Academy of Sciences 117, no. 41 (September 25, 2020): 25655–66. http://dx.doi.org/10.1073/pnas.2011795117.
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Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network (“AI-TAC”) does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.
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Du, Yining. "Intelligent Error Correction of College English Spoken Grammar Based on the GA-MLP-NN Algorithm." Computational Intelligence and Neuroscience 2021 (December 22, 2021): 1–9. http://dx.doi.org/10.1155/2021/7371416.
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With the development of neural networks in deep learning, artificial intelligence machine learning has become the main focus of researchers. In College English grammar detection, oral grammar is the most error rate content. So, this paper optimizes MLP based on GA in the deep learning neural network and then studies the intelligent image correction of College English spoken grammar. The main direction is to discuss and analyze GA-MLP-NN algorithm technology first and then predict the error correction model of spoken language grammar by combining the optimized algorithm. The results show that GA-MLP-NN provides excellent accuracy for the prediction of the whole syntax error correction model. Then, the paper studies the deep learning technology to build an intelligent image error correction model of College English spoken grammar. The results show that the effect of intelligent correction of spoken grammar is very fast and accurate.
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Zeng, Yingpei, Shanqing Guo, Ting Wu, and Qiuhua Zheng. "DSM: Delayed Signature Matching in Deep Packet Inspection." Symmetry 12, no. 12 (December 5, 2020): 2011. http://dx.doi.org/10.3390/sym12122011.
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Deep Packet Inspection (DPI) is widely used in network management and network security systems. The core part of existing DPI is signature matching, and many researchers focus on improving the signature-matching algorithms. In this paper, we work from a different angle: The scheduling of signature matching. We propose a Delayed Signature Matching (DSM) method, in which we do not always immediately match received packets to the signatures since there may be not enough packets received yet. Instead, we predefine some rules, and evaluate the packets against these rules first to decide when to start signature matching and which signatures to match. The predefined rules are convenient to create and maintain since they support custom expressions and statements and can be created in a text rule file. The correctness and performance of the DSM method are theoretically analyzed as well. Finally, we implement a prototype of the DSM method in the open-source DPI library nDPI, and find that it can reduce the signature-matching time about 30∼84% in different datasets, with even smaller memory consumption. Note that the abstract syntax trees (ASTs) used to implement DSM rule evaluation are usually symmetric, and the DSM method supports asymmetric (i.e., single-direction) traffic as well.
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Bui, Nghi D. Q., Yijun Yu, and Lingxiao Jiang. "TreeCaps: Tree-Based Capsule Networks for Source Code Processing." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 30–38. http://dx.doi.org/10.1609/aaai.v35i1.16074.
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Recently program learning techniques have been proposed to process source code based on syntactical structures (e.g., abstract syntax trees) and/or semantic information (e.g., dependency graphs). While graphs may be better than trees at capturing code semantics, constructing the graphs from code inputs through the semantic analysis of multiple viewpoints can lead to inaccurate noises for a specific software engineering task. Compared to graphs, syntax trees are more precisely defined on the grammar and easier to parse; unfortunately, previous tree-based learning techniques have not been able to learn semantic information from trees to achieve better accuracy than graph-based techniques. We have proposed a new learning technique, named TreeCaps, by fusing together capsule networks with tree-based convolutional neural networks to achieve a learning accuracy higher than some existing graph-based techniques while it is based only on trees. TreeCaps introduces novel variable-to-static routing algorithms into the capsule networks to compensate for the loss of previous routing algorithms. Aside from accuracy, we also find that TreeCaps is the most robust to withstand those semantic-preserving program transformations that change code syntax without modifying the semantics. Evaluated on a large number of Java and C/C++ programs, TreeCaps models outperform prior deep learning models of program source code, in terms of both accuracy and robustness for program comprehension tasks such as code functionality classification and function name prediction. Our implementation is publicly available at: https://github.com/bdqnghi/treecaps.
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Mikulová, Marie, Eduard Bejček, Veronika Kolářová, and Jarmila Panevová. "Subcategorization of Adverbial Meanings Based on Corpus Data." Journal of Linguistics/Jazykovedný casopis 68, no. 2 (December 1, 2017): 268–77. http://dx.doi.org/10.1515/jazcas-2017-0036.
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Abstract We introduce a corpus based description of selected adverbial meanings in Czech sentences. Its basic repertory is one of a long lasting tradition in both scientific and school grammars. However, before the corpus era, researchers had to rely on their own excerption; but nowadays, current syntax has a vast material basis in the form of electronic corpora available. On the case of spatial adverbials, we describe our methodology which we used to acquire a detailed, comprehensive, well-arranged description of meanings of adverbials including a list of formal realizations with examples. Theoretical knowledge stemming from this work will lead into an improval of the annotation of the meanings in the Prague Dependency Treebanks which serve as the corpus sources for our research. The Prague Dependency Treebanks include data manually annotated on the layer of deep syntax and thus provide a large amount of valuable examples on the basis of which the meanings of adverbials can be defined more accurately and subcategorized more precisely. Both theoretical and practical results will subsequently be used in NLP, such as machine translation.
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Jinpa, Tenzin, and Yong Gao. "Code Representation Learning Using Prüfer Sequences (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12977–78. http://dx.doi.org/10.1609/aaai.v36i11.21625.
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An effective and efficient encoding of the source code of a computer program is critical to the success of sequence-to-sequence deep neural network models for code representation learning. In this study, we propose to use the Prufer sequence of the Abstract Syntax Tree (AST) of a computer program to design a sequential representation scheme that preserves the structural information in an AST. Our representation makes it possible to develop deep-learning models in which signals carried by lexical tokens in the training examples can be exploited automatically and selectively based on their syntactic role and importance. Unlike other recently-proposed approaches, our representation is concise and lossless in terms of the structural information of the AST. Results from our experiment show that prufer-sequence-based representation is indeed highly effective and efficient.
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Al-Araji, Fatima Ehsan Salih, and Assmaa M. H. Al-moqram. "Hierarchical scaling within the spaces Syntax of the Mosulian house." Association of Arab Universities Journal of Engineering Sciences 28, no. 1 (March 31, 2021): 117–35. http://dx.doi.org/10.33261/jaaru.2021.28.1.011.
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Hierarchical Scaling is one of the Architectural Engineering principles. this principle isdetermined by the apparent scales within the basic elements of architectural design and the nature of thehierarchy in its dimensions. This principle was discussed in previous studies in terms of its relation tonature, its types in different levels of architecture and city.The property of the hierarchical scale wasdiscussed in the previous knowledge in terms of its relation to nature, its types and its general bases at thelevel of architecture and city. Therefore, the need to study this principle appeared at the level of house layoutthrough studying its space syntax in different time periods and its reflection on several aspects.Thus, theresearch problem was determined by "the need to explore the levels of the hierarchical scale within thespace syntax of the houses especially Mosulian house, and its variation within different time periods."The research adopted the quantitative approach consisting of three steps, after introducing generalknowledge base of the concept of the hierarchical scale and the studies that dealt with it in architecture, first:building a theoretical framework on the vocabulary associated with measuring the spatial hierarchical scalewithin the space syntax, second: conducting a practical study on an elected group of Mosulian houses withinfour periods, (20 houses distributed through, traditional, traditional-heritage, transitional andcontemporary),third: analysis of results and identification of conclusions.The research found that theMosulian houses within (the traditional -heritage period) was distinguished by its having a deep spatialhierarchical scale, unlike the Mosulian houses (with a transitional period) that entails many indicators at thelevel of the social dimension in achieving social isolation.
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Shabaev, I. F., R. S. Tarasov, and K. A. Kozyrin. "IN-HOSPITAL RESULTS OF MINIMALLY INVASIVE OFF-PUMP CORONARY ARTERY BYPASS GRAFTING." Complex Issues of Cardiovascular Diseases 8, no. 2 (June 23, 2019): 58–67. http://dx.doi.org/10.17802/2306-1278-2019-8-2-58-67.
Full textAbstract:
Aim. To evaluate the in-hospital results of minimally invasive direct coronary artery bypass grafting (MIDCAB) of the left anterior descending artery (LAD) on the beating heart.Methods. 146 patients with stable coronary artery disease and hemodynamically significant LAD lesions were included in a single-center prospective study. The study endpoints included death, myocardial infarction (MI), stroke, bleedings associated with the CABG, repeated non-elective myocardial revascularization during the in-hospital period. The completeness of the performed revascularization was assessed by estimating the residual SYNTAX score. Additionally, the volume of perioperative blood loss and patients’ ICU and in-hospital length of stay were recorded. Wound complications, heart rhythm and conduction disturbances, respiratory complications were assessed.Results. The mean patient age was 60±8.9 (36–82) years. The mean EuroScore II was 1.61±1.66%. The SYNTAX score was 11.7±9.5 (5–25.5) scores. After MIDCAB, the residual SYNTAX was 1.8±2.1 (0–12.5). The majority of patients had single-vessel disease (n = 108; 73.9%). More than half of the patients had postinfarction cardiosclerosis (PICS). 17.8% of patients had diabetes mellitus. 19% of patients were present with class 1–2 obesity. The mean left ventricular ejection fraction (LVEF) in the total sample was 61.3±7.5% (37–74%). Eight patients (5.5%) required the conversion of lateral minitracotomy to sternotomy followed by the LAD grafting on the beating heart. The mean volume of intraoperative blood loss was 293.4±117.9 mL. Reasonable incomplete myocardial revascularization was achieved in 24.7% of cases. There were no cases of in-hospital deaths in the study cohort. The incidence of MI, stroke and repeated non-elective myocardial revascularization was within the acceptable range of 0.7%, 0% and 2.7%, respectively. 6.1% of patients (n = 9) had wound complications (deep incisional surgical site infection). The mean length of stay in the intensive care unit was 1±0.2 days. The absolute majority of patients (n = 108) were discharged from the Department of Cardiac Surgery in the period of 10±1.2 days.Conclusion. Off-pump MIDCAB demonstrated good results, low rate of cardiovascular complications and low volume of perioperative blood loss, lack of deep wound infection and shorter hospital length of stay.