Syntactic Types Research Articles

Consistency of a Dependent Calculus of Indistinguishability

The Dependent Calculus of Indistinguishability (DCOI) uses dependency tracking to identify irrelevant arguments and uses indistinguishability during type conversion to enable proof irrelevance, supporting run-time and compile-time irrelevance with the same uniform mechanism. DCOI also internalizes reasoning about indistinguishability through the use of a propositional equality type indexed by an observer level. As DCOI is a pure type system, prior work establishes only its syntactic type safety, justifying its use as the basis for a programming language with dependent types. However, it was not clear whether any instance of this system would be suitable for use as a type theory for theorem proving. Here, we identify a suitable instance DCOI ω , which has an infinite predicative universe hierarchy. We show that DCOI ω is logically consistent, normalizing, and that type conversion is decidable. We have mechanized all results using the Coq proof assistant.

A Logical Approach to Type Soundness

Type soundness, which asserts that “well-typed programs cannot go wrong,” is widely viewed as the canonical theorem one must prove to establish that a type system is doing its job. It is commonly proved using the so-called syntactic approach (also known as progress and preservation ), which has had a huge impact on the study and teaching of programming language foundations. Unfortunately, syntactic type soundness is a rather weak theorem. It only applies to programs that are well typed in their entirety and thus tells us nothing about the many programs written in “safe” languages that make use of “unsafe” language features. Even worse, it tells us nothing about whether type systems achieve one of their main goals: enforcement of data abstraction. One can easily define a language that enjoys syntactic type soundness and yet fails to support even the most basic modular reasoning principles for abstraction mechanisms like closures, objects, and abstract data types. Given these concerns, we argue that programming languages researchers should no longer be satisfied with proving syntactic type soundness and should instead start proving semantic type soundness , a more useful theorem that captures more accurately what type systems are actually good for. Semantic type soundness is an old idea—Milner’s original account of type soundness from 1978 was semantic—but it fell out of favor in the 1990s due to limitations and complexities of denotational models. In the succeeding decades, thanks to a series of technical advances—notably, step-indexed Kripke logical relations constructed over operational semantics and higher-order concurrent separation logic as consolidated in the Iris framework in Coq—we can now build (machine-checked) semantic soundness proofs at a much higher level of abstraction than was previously possible. The resulting “logical” approach to semantic type soundness has already been employed to great effect in a number of recent papers, but those papers typically (a) concern advanced problem scenarios that complicate the presentation, (b) assume significant prior knowledge of the reader, and (c) suppress many details of the proofs. Here, we aim to provide a gentler, more pedagogically motivated introduction to logical type soundness, targeted at a broader audience that may or may not be familiar with logical relations and Iris. As a bonus, we also show how logical type soundness proofs can easily be generalized to establish an even stronger relational property— representation independence —for realistic type systems.

A Functional Magnetic Resonance Imaging Study of Phrase Structure and Subject Island Violations.

In principle, functional neuroimaging provides uniquely informative data in addressing linguistic questions, because it can indicate distinct processes that are not apparent from behavioral data alone. This could involve adjudicating the source of unacceptability via the different patterns of elicited brain responses to different ungrammatical sentence types. However, it is difficult to interpret brain activations to syntactic violations. Such responses could reflect processes that have nothing intrinsically related to linguistic representations, such as domain-general executive function abilities. To facilitate the potential use of functional neuroimaging methods to identify the source of different syntactic violations, we conducted a functional magnetic resonance imaging experiment to identify the brain activation maps associated with two distinct syntactic violation types: phrase structure (created by inverting the order of two adjacent words within a sentence) and subject islands (created by extracting a wh-phrase out of an embedded subject). The comparison of these violations to control sentences surprisingly showed no indication of a generalized violation response, with almost completely divergent activation patterns. Phrase structure violations seemingly activated regions previously implicated in verbal working memory and structural complexity in sentence processing, whereas the subject islands appeared to activate regions previously implicated in conceptual-semantic processing, broadly defined. We review our findings in the context of previous research on syntactic and semantic violations using ERPs. Although our results suggest potentially distinct underlying mechanisms underlying phrase structure and subject island violations, our results are tentative and suggest important methodological considerations for future research in this area.

An fMRI study of phrase structure and subject island violations.

In principle, functional neuroimaging provides uniquely informative data in addressing linguistic questions, because it can indicate distinct processes that are not apparent from behavioral data alone. This could involve adjudicating the source of unacceptability via the different patterns of elicited brain responses to different ungrammatical sentence types. However, it is difficult to interpret brain activations to syntactic violations. Such responses could reflect processes that have nothing intrinsically related to linguistic representations, such as domain-general executive function abilities. In order to facilitate the potential use of functional neuroimaging methods to identify the source of different syntactic violations, we conducted an fMRI experiment to identify the brain activation maps associated with two distinct syntactic violation types: phrase structure (created by inverting the order of two adjacent words within a sentence) and subject islands (created by extracting a wh-phrase out of an embedded subject). The comparison of these violations to control sentences surprisingly showed no indication of a generalized violation response, with almost completely divergent activation patterns. Phrase structure violations seemingly activated regions previously implicated in verbal working memory and structural complexity in sentence processing, whereas the subject islands appeared to activate regions previously implicated in conceptual-semantic processing, broadly defined. We review our findings in the context of previous research on syntactic and semantic violations using event-related potentials. Although our results suggest potentially distinct underlying mechanisms underlying phrase structure and subject island violations, our results are tentative and suggest important methodological considerations for future research in this area.

Law and Order for Typestate with Borrowing

Typestate systems are notoriously complex as they require sophisticated machinery for tracking aliasing. We propose a new, transition-oriented foundation for typestate in the setting of impure functional programming. Our approach relies on ordered types for simple alias tracking and its formalization draws on work on bunched implications. Yet, we support a flexible notion of borrowing in the presence of typestate. Our core calculus comes with a notion of resource types indexed by an ordered partial monoid that models abstract state transitions. We prove syntactic type soundness with respect to a resource-instrumented semantics. We give an algorithmic version of our type system and prove its soundness. Algorithmic typing facilitates a simple surface language that does not expose tedious details of ordered types. We implemented a typechecker for the surface language along with an interpreter for the core language.

Staged Compilation with Module Functors

Multi-stage programming has been used in a wide variety of domains to eliminate the tension between abstraction and performance. However, the interaction of multi-stage programming features with features for programming-in-the-large remains understudied, hindering the full integration of multi-stage programming support into existing languages, and limiting the effective use of staging in large programs. We take steps to remedy the situation by studying the extension of MacoCaml, a recent OCaml extension that supports compile-time code generation via macros and quotations , with module functors, the key mechanism in OCaml for assembling program components into larger units. We discuss design choices related to evaluation order, formalize our calculus via elaboration, and show that the design enjoys key metatheoretical properties: syntactic type soundness, elaboration soundness, and phase distinction. We believe that this study lays a foundation for the continued exploration and implementation of the OCaml macro system.

Parallel Algebraic Effect Handlers

Algebraic effect handlers support composable and structured control-flow abstraction. However, existing designs of algebraic effects often require effects to be executed sequentially. This paper studies parallel algebraic effect handlers. In particular, we formalize λ p , a lambda calculus which models two key features, effect handlers and parallelizable computations, the latter of which takes the form of a for expression, inspired by the Dex programming language. We present various interesting examples expressible in our calculus. To show that our design can be implemented in a type-safe way, we present a higher-order polymorphic lambda calculus F p that extends λ p with a lightweight value dependent type system, and prove that F p preserves the semantics of λ p and enjoys syntactic type soundness. Lastly, we provide an implementation of the language design as a Haskell library, which mirrors both λ p and F p and reveals new connections to free applicative functors. All examples presented can be encoded in the Haskell implementation. We believe this paper is the first to study the combination of user-defined effect handlers and parallel computations, and it is our hope that it provides a basis for future designs and implementations of parallel algebraic effect handlers.

CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection

With the development of the open source community, the code is often copied, spread, and evolved in multiple software systems, which brings uncertainty and risk to the software system (e.g., bug propagation and copyright infringement). Therefore, it is important to conduct code clone detection to discover similar code pairs. Many approaches have been proposed to detect code clones where token-based tools can scale to big code. However, due to the lack of program details, they cannot handle more complicated code clones, i.e., semantic code clones. In this paper, we introduce CC2Vec, a novel code encoding method designed to swiftly identify simple code clones while also enhancing the capability for semantic code clone detection. To retain the program details between tokens, CC2Vec divides them into different categories (i.e., typed tokens) according to the syntactic types and then applies two self-attention mechanism layers to encode them. To resist changes in the code structure of semantic code clones, CC2Vec performs contrastive learning to reduce the differences introduced by different code implementations. We evaluate CC2Vec on two widely used datasets (i.e., BigCloneBench and Google Code Jam) and the results report that our method can effectively detect simple code clones. In addition, CC2Vec not only attains comparable performance to widely used semantic code clone detection systems such as ASTNN, SCDetector, and FCCA by simply fine-tuning, but also significantly surpasses these methods in both detection efficiency.

Caregiver and child question types during a museum interaction.

Children museums provide an engaging learning environment for families with exhibits designed to stimulate caregiver-child interactions. Specific types of questions have been shown to support child language learning by scaffolding more elaborative responses. This study analyzed the use of question form types during caregiver-child interactions in a children's museum, aiming to discern their correlation with child language proficiency. We examined and transcribed two exhibit explorations by 43 caregiver-child dyads (3- to 6-year-old children). Our analysis encompasses various syntactic question types (e.g., yes-no, wh-) and measures of child language proficiency, including lexical diversity, morphosyntactic complexity, and overall language ability. Findings reveal disparities in question form usage among caregivers and children, with caregivers predominantly employing closed questions and children balancing closed and open-ended types. Children of caregivers who predominantly posed closed questions exhibited shorter utterances and lower overall language scores. Details on other question forms are presented (sub-types of polar, wh-, alternative, and echo). These findings contribute to our understanding of how question form influences language development and caregiver-child interactions.

Location-enhanced syntactic knowledge for biomedical relation extraction

Biomedical relation extraction has long been considered a challenging task due to the specialization and complexity of biomedical texts. Syntactic knowledge has been widely employed in existing research to enhance relation extraction, providing guidance for the semantic understanding and text representation of models. However, the utilization of syntactic knowledge in most studies is not exhaustive, and there is often a lack of fine-grained noise reduction, leading to confusion in relation classification. In this paper, we propose an attention generator that comprehensively considers both syntactic dependency type information and syntactic position information to distinguish the importance of different dependency connections. Additionally, we integrate positional information, dependency type information, and word representations together to introduce location-enhanced syntactic knowledge for guiding our biomedical relation extraction. Experimental results on three widely used English benchmark datasets in the biomedical domain consistently outperform a range of baseline models, demonstrating that our approach not only makes full use of syntactic knowledge but also effectively reduces the impact of noisy words.

Lead News Coverage of Buddhist Monks in The Nation Online Newspaper: A Syntactic Analysis

This research conducts Lead News Coverage of Buddhist Monks in The Nation Online Newspaper: A Syntactic Analysis. It delves into syntactic sentence types, phrase-level structures in subjects and predicates, and word-level structures within the same context. The qualitative study utilizes data collected from The Nation's website over a decade (2013–2023) to analyze sentence types categorized into simple, compound, complex, and compound-complex, revealing a preference for simple (51.79%) and complex sentences (39.29%), indicating a journalistic inclination towards clarity and in-depth information presentation. Phrase-level analysis highlights a dominance of Noun Phrases (43.26%) and Prepositional Phrases (32.01%), underscoring their role in establishing relational context and subjects in sentences. Word-level analysis uncovers a significant use of determiners (44.07%) and nouns (38.45%), emphasizing their essential roles in conveying meaning and specifying noun phrases. The study's findings suggest a strategic balance in The Nation's syntactic choices, reflecting a nuanced approach to ensuring readability while providing comprehensive information, aligning with broader trends in digital journalism. This research contributes to understanding the syntactic strategies in journalism, particularly in the context of online media and its representation of religious figures.

Linguistic Characteristics of Research Article Titles in National and International ELT Journals

Researchers have extensively studied the linguistic characteristics of research article (RA) titles. However, no research has investigated RA titles viewed from research design types published in national and international English Language Teaching (ELT) journals. Thus, the present study aims to fill this gap by investigating the average word length, syntactic structure, and syntactic structure type based on research design. This study used a comparative mixed method design to analyze 120 RA titles published in 2022-2023. The average length of titles was analyzed by counting the parts of speech of the tiles, the syntactic structures of titles were analyzed using the Framework of Cheng et al. (2012), and syntactic structure types based on research design were analyzed by reading the method section of the articles. Results indicate that the average length of the RA titles was 12 words in national journals and 13 words in international journals. Nominal structure was the most used RA title type in national journals, while compound structure was the most used in international journals. Furthermore, the nominal structure was often used for experimental design in international journals, while it was often used for descriptive study design in national journals. The compound and Verb-ing phrase structures were often used for case study design in both international and national journals. In conclusion, there are important differences and similarities in RA title characteristics between the two groups of ELT journal rankings. The findings contribute to the scholarship on the contrastive aspect of titling practices in English for Publishing Purposes.

КЕЙС ЛІНГВАЛЬНОГО ПРОФЕСІЙНОГО ПРОФІЛЮВАННЯ: СИНТАКСИЧНІ АСПЕКТИ МОВИ ВЧИТЕЛЯ ІНОЗЕМНОЇ МОВИ

The article examines the issue of linguistic profiling of individuals through the lens of their career and language competence. The author aims to identify a set of characteristic speech markers typical of the real and virtual English language teachers and contrasts the results obtained. Thus, the present study is methodologically based on the original English-language novels where the English language teachers are depicted as characters, on the one hand, and on a field experiment conducted personally by the author and involving two Ukraine-originating teachers of English from Odesa lyceums. Having analysed the notions of professional image and speech portrait, the author outlines the English language teacher’s speech status in society in general and proves them to be representatives of the elite social stratum. The study has an uncontestable scientific novelty since it has revealed that whilst the real teachers of English from Ukraine and their colleagues described in the original English-language novels demonstrate similar syntactic sentence types in verbal communication, the differences are also quite striking and possibly rooted in the national mentality. Thus, a conclusion has been made that virtual English teachers prefer encoding their information in complicated sentences, using syntactic complexes, whereas the real teachers of English from Ukraine, unfortunately, neglect this way of minimizing speech efforts, and employ more complex and composite sentences instead. Another interesting discovery is the virtual teachers’ tendency to ask several tag-questions in a row, which can be seen as a way of verbal suggestion to a certain answer by the interlocutor. Mistakes being far from typical in the speech of both groups, the Ukrainian teachers of English may still deviate from the traditional rules of word order and overload the linear length of their sentences. Regarding the perspectives of the study, the author plans to contrast the speech of virtual English teachers with that of real English teachers from the English-speaking countries, both male and female, and to add the levels of phonetics and vocabulary to the scope of analysis.

Dependent Type Refinements for Futures

Type refinements combine the compositionality of typechecking with the expressivity of program logics, offering a synergistic approach to program verification. In this paper we apply dependent type refinements to SAX, a futures-based process calculus that arises from the Curry-Howard interpretation of the intuitionistic semi-axiomatic sequent calculus and includes unrestricted recursion both at the level of types and processes. With our type refinement system, we can reason about the partial correctness of SAX programs, complementing prior work on sized type refinements that supports reasoning about termination. Our design regime synthesizes the infinitary proof theory of SAX with that of bidirectional typing and Hoare logic, deriving some standard reasoning principles for data and (co)recursion while enabling information hiding for codata. We prove syntactic type soundness, which entails a notion of partial correctness that respects codata encapsulation. We illustrate our language through a few simple examples.Comment: 15 pages, MFPS 2023

English Translation Stylistic Features and Syntax Translation with Application of Knowledge Mapping

Abstract Aiming at the problems of excessive instantiation complexity, poor interpretability and low generalization ability of knowledge reasoning techniques, this paper unites inductive logic programming ILP with HET neural network to construct a hybrid logic rule and neural network knowledge graph reasoning model - HETIL model. The ILP is utilized to quantify the first-order logic rules, and the multi-layer rule space is constructed by arranging and combining the rules through logic symbols. The rules are instantiated and fed into the HET network, and the attention coefficients aggregate the node features to complete the end-to-end training and generate the rule learning model. Finally, the validity of the model is verified by machine translation experiments, and the results show that the accuracy of the HETIL model in syntactic structure types is more than 0.8 overall. The accuracy in terms of phrase structure reaches 0.879. The average BLEU value of the HETIL model can reach 29.24, which is 1.51 BLEU points higher than the benchmark model. Therefore, the effect of English translation by applying a knowledge graph is better than traditional machine translation.

Особенности языковой игры в современных самарских эргонимах сферы «Отдых и развлечения»

The aim of the study is to shed light on the peculiarities of wordplay in ergonyms of the Samara region belonging to the socio-cultural sphere. The author pays attention to the identification of wordplay techniques at the level of phonetics, lexis, syntax and word formation; analyzes the process of artificial naming, trying to find out whether the sphere of activity affects the choice of wordplay types and techniques. The scientific novelty is associated with the fact that this problem is little studied in terms of identifying wordplay types and techniques taking into account the specifics of the activity of an enterprise or an organization. The analysis of the language material suggests, on the one hand, the high degree of creativity of wordplay in the ergonyms of the “Recreation and entertainment” sphere and, on the other hand, the variety of techniques at different language levels. As a result of the study, four types of wordplay correlated with the language levels, namely, phonetics, lexis, syntax and word formation, were identified. Twenty-two wordplay techniques were noted at the specified language levels: five techniques were attributed to the phonetic type of wordplay, four techniques were found in the lexical type of wordplay, five techniques were attributed to the syntactic type of wordplay and eight techniques were characterized as the word-forming type of wordplay.

The Analysis of the Syntactic Interference and the Ways of Its Expression in a Text on the Example of Spanglish

The problem of syntactic interference, in particular, of its expression in a text, is being investigated. As the example, the syntax of such mixed language as Spanglish that is based on the interference and the interaction of the English and the Spanish languages is being analyzed. To illustrate the subtypes of the syntactic interference, a number of literary works written in Spanglish have been analyzed, on the basis of which patterns have been derived in the manifestation of the syntactic interference types in this mixed language.

MIFINN: A novel multi-information fusion and interaction neural network for aspect-based sentiment analysis

Aspect-based Sentiment Analysis (ABSA) is used to detect corresponding sentiment polarities toward different aspect terms. In recent years, graph convolutional network-based methods achieve great success for ABSA. However, previous studies ignore syntactic dependency types and aspect-related syntactic distances. Besides, effectively fusing semantic and syntactic information remains a challenging problem. Previous studies also neglect interactions between aspect terms and context. To alleviate the above problems, we develop a Multi-Information Fusion and Interaction Neural Network (MIFINN), which consists of an Aspect and Syntax-Aware Multi-Information Fusion Graph Convolutional Network (ASAMIFGCN) and a Multi-Information Interaction and Gating Network (MIIGN). The ASAMIFGCN model can integrate and fuse contextual semantics, syntactic dependency types, and aspect-related syntactic distances. Specifically, two different attentions are designed to learn global and aspect-related semantic and syntactic information, respectively. A syntactic distance gating module is proposed to acquire more precise score matrices. Besides, we propose a semantic and syntactic association module to fuse multi-information simultaneously. The MIIGN model can effectively interact aspect terms and context. Specifically, two interactive attentions are developed to learn different weights toward aspect terms and context via interactions between each other, respectively. Moreover, we propose a gating information module for controlling the information flow of aspect terms and context. We conduct experiments on multiple datasets and MIFINN achieves state-of-the-art performance. The results also demonstrate that effectively fusing and interacting multi-information can improve model performance for ABSA significantly.

Reading comprehension performance of elementary and senior high school students

IntroductionIn Brazil, reading has been widely discussed, mainly due to the published results of national and international performance exams of Brazilian schoolchildren. Learning to read is therefore a continuous process, and the ability to make inferences while reading a text develops with age. The textual complexity involving the syntactic structuring, vocabulary and types of text progressively increases from the initial years of elementary school to high school students, also increasing the cognitive demand of the students; this occurs in parallel to their development and school advancement, which allows improvements in their teaching/learning processes. Based on the above, the following questions were raised: (1) How is the semantic process of reading established among elementary school students in elementary school (cycle II) and high school? Aim: to characterize the performance of elementary and senior high school students on semantic process tests from the Brazilian adaptation of the evaluation of reading processes (PROLEC-SE-R).MethodsA total of 436 students of both sexes, aged between 11 and 18 years, participated. They were evaluated with Assessment of Reading Processes-PROLEC-SE-R.Results and discussionThe results indicated that the semantic process was equally established among high school students, with a higher average performance compared with that of elementary school students. Among elementary school students, there was progression in the average correct answers with advancement in schooling. In the two levels of education, narrative texts allowed a greater number of correct answers, followed by multiple-choice and literal questions. The PROLEC-SE-R semantic process tests proved to be effective for assessing reading comprehension in elementary and high school students and reflected the Brazilian reality with regard to the gaps and weaknesses in the educational system.

An effective text plagiarism detection system based on feature selection and SVM techniques

Text plagiarism has greatly spread in the recent years, it becomes a common problem in several fields such as research manuscripts, textbooks, patents, academic circles, etc. There are many sentence similarity features were used to detect plagiarism, but each of them is not discriminative to differentiate the similarity cases. This causes the discovery of lexical, syntactic and semantic text plagiarism types to be a challenging problem. Therefore, a new plagiarism detection system is proposed to extract the most effective sentence similarity features and construct hyperplane equation of the selected features to distinguish the similarity cases with the highest accuracy. It consists of three phases; the first phase is used to preprocess the documents. The second phase is depended on two paths, the first path is based on traditional paragraph level comparison, and the second path is based on the computed hyperplane equation using Support Vector Machine (SVM) and Chi-square techniques. The third phase is used to extract the best plagiarized segment. The proposed system is evaluated on several benchmark datasets. The experimental results showed that the proposed system obtained a significant superiority in the performance compared to the systems with a higher ranking in the recent years. The proposed system achieved the best values 89.12% and 92.91% of the Plagdet scores, 89.34% and 92.95% of the F-measure scores on the complete test corpus of PAN 2013 and PAN 2014 datasets, respectively.