Semantics Of Language Research Articles

Atypical Brain Connectivity During Pragmatic and Semantic Language Processing in Children with Autism

Background/Objectives: Children with Autism Spectrum Disorder (ASD) face challenges in social communication due to difficulties in considering context, processing information, and interpreting social cues. This study aims to explore the neural processes related to pragmatic language communication in children with ASD and address the research question of how functional brain connectivity operates during complex pragmatic language tasks. Methods: We examined differences in brain functional connectivity between children with ASD and typically developing peers while they engaged in video recordings of spoken language tasks. We focused on two types of speech acts: semantic and pragmatic. Results: Our results showed differences between groups during the pragmatic and semantic language processing, indicating more idiosyncratic connectivity in children with ASD in the Left Somatomotor and Left Limbic networks, suggesting that these networks play a role in task-dependent functional connectivity. Additionally, these functional differences were mainly localized to the left hemisphere.

Towards a Computational Theory of Perceptions under Neutrosophic Environment

Computing and reasoning with perceptions in AI are shifting from traditional methods to those that mimic human cognitive processes. Humans intuitively measure and evaluate without explicit computations, using sensory input and past experiences for quick judgments. The theory of perceptions can enhance AI's ability to replicate human-like assessment by providing a framework for handling the nuances of human reasoning. Traditionally, information granularity is either crisp (c-granular) or fuzzy (f-granular), focusing on measurement-based and perception-based information. This paper outlines a neutrosophic-based computational theory of perceptions (n-CTP), introducing neutrosophic granular (n-granular) information and neutrosophic constraint-centered semantics of natural languages (n-CSNL). Based on the neutrosophy theory, which deals with truth, falsity, and indeterminacy, managing uncertainties and vagueness in real-world data can be done more effectively than with traditional methods. The n-CTP has the potential to enhance AI's ability to process and reason with complex, uncertain, and ambiguous information.

Automating Pruning in Top-Down Enumeration for Program Synthesis Problems with Monotonic Semantics

In top-down enumeration for program synthesis, abstraction-based pruning uses an abstract domain to approximate the set of possible values that a partial program, when completed, can output on a given input. If the set does not contain the desired output, the partial program and all its possible completions can be pruned. In its general form, abstraction-based pruning requires manually designed, domain-specific abstract domains and semantics, and thus has only been used in domain-specific synthesizers. This paper provides sufficient conditions under which a form of abstraction-based pruning can be automated for arbitrary synthesis problems in the general-purpose Semantics-Guided Synthesis (SemGuS) framework without requiring manually-defined abstract domains. We show that if the semantics of the language for which we are synthesizing programs exhibits some monotonicity properties, one can obtain an abstract interval-based semantics for free from the concrete semantics of the programming language, and use such semantics to effectively prune the search space. We also identify a condition that ensures such abstract semantics can be used to compute a precise abstraction of the set of values that a program derivable from a given hole in a partial program can produce. These precise abstractions make abstraction-based pruning more effective. We implement our approach in a tool, Moito, which can tackle synthesis problems defined in the SemGuS framework. Moito can automate interval-based pruning without any a-priori knowledge of the problem domain, and solve synthesis problems that previously required domain-specific, abstraction-based synthesizers— e.g., synthesis of regular expressions, CSV file schema, and imperative programs from examples.

Semantics Lifting for Syntactic Sugar

Syntactic sugar plays a crucial role in engineering programming languages. It offers convenient syntax and higher-level of abstractions, as witnessed by its pervasive use in both general-purpose and domain-specific contexts. Unfortunately, the traditional approach of translating programs containing syntactic sugars into the host language can lead to abstraction leakage, breaking the promise of convenience and hindering program comprehension. To address this challenge, we introduce the idea of semantics lifting that aims to statically derive self-contained evaluation rules for syntactic sugars. More specifically, we propose a semantics-lifting framework that consists of (i) a general algorithm for deriving host-independent semantics of syntactic sugars from the semantics of the host language and the desugaring rules, (ii) a formulation of the correctness and abstraction properties for a lifted semantics, and (iii) a systematic investigation of sufficient conditions that ensure a lifted semantics is provably correct and abstract. To evaluate our semantics-lifting framework, we have implemented a system named Osazone and conducted several case studies, demonstrating that our approach is flexible, effective, and practical for implementing domain-specific languages.

Domain Specific Abstractions for the Development of Fast-by-Construction Dataflow Codes on FPGAs

FPGAs are popular in many fields but have yet to gain wide acceptance for accelerating HPC codes. A major cause is that whilst the growth of High-Level Synthesis (HLS), enabling the use of C or C++, has increased accessibility, without widespread algorithmic changes these tools only provide correct-by-construction rather than fast-by-construction programming. The fundamental issue is that HLS presents a Von Neumann-based execution model that is poorly suited to FPGAs, resulting in a significant disconnect between HLS’s language semantics and how experienced FPGA programmers structure dataflow algorithms to exploit hardware. We have developed the high-level language Lucent which builds on principles previously developed for programming general-purpose dataflow architectures. Using Lucent as a vehicle, in this paper we explore appropriate abstractions for developing application-specific dataflow machines on reconfigurable architectures. The result is an approach enabling fast-by-construction programming for FPGAs, delivering competitive performance against hand-optimised HLS codes whilst significantly enhancing programmer productivity.

Development of a Children’s Educational Dictionary for a Low-Resource Language Using AI Tools

Today, various interactive tools or partially available artificial intelligence applications are actively used in educational processes to solve multiple problems for resource-rich languages, such as English, Spanish, French, etc. Unfortunately, the situation is different and more complex for low-resource languages, like Kazakh, Uzbek, Mongolian, and others, due to the lack of qualitative and accessible resources, morphological complexity, and the semantics of agglutinative languages. This article presents research on early childhood learning resources for the low-resource Kazakh language. Generally, a dictionary for children differs from classical educational dictionaries. The difference between dictionaries for children and adults lies in their purpose and methods of presenting information. A themed dictionary will make learning and remembering new words easier for children because they will be presented in a specific context. This article discusses developing an approach to creating a thematic children’s dictionary of the low-resource Kazakh language using artificial intelligence. The proposed approach is based on several important stages: the initial formation of a list of English words with the use of ChatGPT; identification of their semantic weights; generation of phrases and sentences with the use of the list of semantically related words; translation of obtained phrases and sentences from English to Kazakh, dividing them into bigrams and trigrams; and processing with Kazakh language POS pattern tag templates to adapt them for children. When the dictionary was formed, the semantic proximity of words and phrases to the given theme and age restrictions for children were taken into account. The formed dictionary phrases were evaluated using the cosine similarity, Euclidean similarity, and Manhattan distance metrics. Moreover, the dictionary was extended with video and audio data by implementing models like DALL-E 3, Midjourney, and Stable Diffusion to illustrate the dictionary data and TTS (Text to Speech) technology for the Kazakh language for voice synthesis. The developed thematic dictionary approach was tested, and a SUS (System Usability Scale) assessment of the application was conducted. The experimental results demonstrate the proposed approach’s high efficiency and its potential for wide use in educational purposes.

Referring Image Segmentation With Fine-Grained Semantic Funneling Infusion.

Recently, referring image segmentation has attracted wide attention given its huge potential in human-robot interaction. Networks to identify the referred region must have a deep understanding of both the image and language semantics. To do so, existing works tend to design various mechanisms to achieve cross-modality fusion, for example, tile and concatenation and vanilla nonlocal manipulation. However, the plain fusion usually is either coarse or constrained by the exorbitant computation overhead, finally causing not enough understanding of the referent. In this work, we propose a fine-grained semantic funneling infusion (FSFI) mechanism to solve the problem. The FSFI introduces a constant spatial constraint on the querying entities from different encoding stages and dynamically infuses the gleaned language semantic into the vision branch. Moreover, it decomposes the features from different modalities into more delicate components, allowing the fusion to happen in multiple low-dimensional spaces. The fusion is more effective than the one only happening in one high-dimensional space, given its ability to sink more representative information along the channel dimension. Another problem haunting the task is that the instilling of high-abstract semantic will blur the details of the referent. Targetedly, we propose a multiscale attention-enhanced decoder (MAED) to alleviate the problem. We design a detail enhancement operator (DeEh) and apply it in a multiscale and progressive way. Features from the higher level are used to generate attention guidance to enlighten the lower-level features to more attend to the detail regions. Extensive results on the challenging benchmarks show that our network performs favorably against the state-of-the-arts (SOTAs).

A language-agnostic model of child language acquisition

This work reimplements a recent semantic bootstrapping child language acquisition (CLA) model, which was originally designed for English, and trains it to learn a new language: Hebrew. The model learns from pairs of utterances and logical forms as meaning representations, and acquires both syntax and word meanings simultaneously. The results show that the model mostly transfers to Hebrew, but that a number of factors, including the richer morphology in Hebrew, makes the learning slower and less robust. This suggests that a clear direction for future work is to enable the model to leverage the similarities between different word forms.

Functional-referential aspects of church-related lexis with object semantics in the Russian language (as exemplified by the names of church utensils)

Functional-referential aspects of church-related lexis with object semantics in the Russian language (as exemplified by the names of church utensils)

Propositional logic and modal logic—A connection via relational semantics

Abstract In this paper, by slightly generalizing an observation of Dalla Chiara and Giuntini in their chapter on quantum logic in Handbook of Philosophical Logic, we propose a relational semantics for propositional language with negation and conjunction, which unifies the relational semantics of intuitionistic logic and that of ortho-logic. We study the semantic and syntactic consequence relations and prove the soundness and completeness theorems for five propositional logics: $\textbf{BL}$, $\textbf{PL}$, $\textbf{IL}$, $\textbf{OL}$ and $\textbf{CL}$. Moreover, we prove that they can be translated into the modal logics $\textbf{K}$, $\textbf{T}$, $\textbf{S4}$, $\textbf{KTB}$ and $\textbf{S5}$, respectively, and thus establish a systematic connection between propositional logics and modal logics. The paper ends with a discussion about the possibility and difficulty of incorporating disjunction into our framework.

Is Alzheimer’s Disease a Cross-Linguistic Issue? Comparing Corpora from a Role and Reference Grammar Perspective

Taking Role and Reference Grammar (Van Valin y LaPolla, 1997; Van Valin, 2005) as theoretical framework, we aim to compare three corpora of patients with Alzheimer’s disease: two in Spanish — PerLA (Pérez Mantero, 2014) and Peraita & Grasso (2010) — and one in English — Pittsburgh (Becker et al., 1994). This grammar is part of the functionalist approaches to language and it provides a series of ordered steps that connect syntax and semantics, as well as incorporating discourse-pragmatics. Thus, we compare the results of the three corpora after applying this syntax-semantics interface to a representative sample with the aim of verifying how Alzheimer’s disease behaves in the two languages. Our results show that there are no significant differences in syntax and semantics in both languages, and that pragmatics plays a more important role than expected at first.

Less is more: A closer look at semantic-based few-shot learning

Few-shot Learning (FSL) aims to learn and distinguish new categories from a scant number of available samples, presenting a significant challenge in the realm of deep learning. Recent researchers have sought to leverage the additional semantic or linguistic information of scarce categories with a pre-trained language model to facilitate learning, thus partially alleviating the problem of insufficient supervision signals. Nonetheless, the full potential of the semantic information and pre-trained language model have been underestimated in the few-shot learning till now, resulting in limited performance enhancements. To address this, we propose a straightforward and efficacious framework for few-shot learning tasks, specifically designed to exploit the semantic information and language model. Specifically, we explicitly harness the zero-shot capability of the pre-trained language model with learnable prompts. And we directly add the visual feature with the textual feature for inference without the intricate designed fusion modules as in prior studies. Additionally, we apply the self-ensemble and distillation to further enhance performance. Extensive experiments conducted across four widely used few-shot datasets demonstrate that our simple framework achieves impressive results. Particularly noteworthy is its outstanding performance in the 1-shot learning task, surpassing the current state-of-the-art by an average of 3.3% in classification accuracy. Our code will be available at https://github.com/zhouchunpong/SimpleFewShot.

Multifaceted formal methods and their interdisciplinary role — From the cathedral of ‘components as coalgebras’ to the HCI context and the open source software bazaar

In this article we revisit the history of formal methods with a focus on important aspects that contribute to their interdisciplinary role. We consider: the variability of mathematical representation techniques on which the theoretical foundations of formal methods are based; formal methods multidisciplinarity; their capability to serve at a meta-level in providing the semantics of programming languages, specification and modelling languages as well as higher-level and domain-specific formal notations; and, finally, how some of these higher-level and domain-specific notations may be lifted at an interdisciplinary level.Within this historical review, we are inspired by Luís Barbosa's “components as coalgebras” approach in seeing that the duality data-process is underlying all those aspects of formal methods. We also see that such a duality may not only be expressed in universal terms within category theory, but may also be characterised in practical terms and focused applications by two distinct logic paradigms, equational logic for the data and rewriting logic for the process, by two modelling directions, forward process definitions and backward data-driven process transformations, and by the distinction between syntax, defined by the data structures, and semantics, provided by rewrite rules. We use the Maude modelling language to illustrate the application of the data-process duality. In fact, Maude use equational logic to define data types and rewriting logic to express system evolution. Illustrative examples are from the areas of cognitive science and human-computer interaction (HCI).We then define a data-driven model transformation, which we call elaborative mining, which adopts a backward perspective to recover a behaviour that was observed in real life but was not predicted forward by the original model. Finally, we see how the “open source software bazaar”, which is a metaphor for the apparently chaotic open source development process, offers us a big data context to lift the driving process for model transformation from deterministic to statistical.

Transformer-Based Sequence Modeling Short Answer Assessment Framework

Automated subjective assessment presents a significant challenge due to the complex nature of human language and reasoning characterized by semantic variability, subjectivity, language ambiguity, and judgment levels. Unlike objective exams, subjective assessments involve diverse answers, posing difficulties in automated scoring. The paper proposes a novel approach that integrates advanced natural language processing (NLP) techniques with principled grading methods to address this challenge. Combining Transformer-based Sequence Language Modeling with sophisticated grading mechanisms aims to develop more accurate and efficient automatic grading systems for subjective assessments in education. The proposed approach consists of three main phases: Content Summarization: Relevant sentences are extracted using self-attention mechanisms, enabling the system to effectively summarize the content of the responses. Key Term Identification and Comparison: Key terms are identified within the responses and treated as overt tags. These tags are then compared to reference keys using cross-attention mechanisms, allowing for a nuanced evaluation of the response content. Grading Process: Responses are graded using a weighted multi-criteria decision method, which assesses various quality aspects and assigns partial scores accordingly. Experimental results on the SQUAD dataset demonstrate the approach’s effectiveness, achieving an impressive F-score of 86%. Furthermore, significant improvements in metrics like ROUGE, BLEU, and METEOR scores were observed, validating the efficacy of the proposed approach in automating subjective assessment tasks. Doi: 10.28991/HIJ-2024-05-03-06 Full Text: PDF

A novel service composition algorithm using natural language semantics

A novel service composition algorithm using natural language semantics

Toward a historical grammar of elevation in Kartvelian

This paper in detail addresses and, for the first time in the field, presents linguistic evidence related to the expression of elevational semantics in the Kartvelian languages. The domains of its expression include a wide range of parts of speech, such as adverbs, adjectives, postpositions, and verbs. Provided with a broad synchronic illustrative base, as well as with respective diachronic commentaries, the material under discussion is meant to lay the groundwork for the establishment of a diachronic grammar of elevation in Kartvelian.

Multi-modal Chinese Text Emotion Metaphor Computation Based on Mutual Information and Information Entropy

Metaphor is to express another thing through one thing, it is not only a rhetorical means, but also embodies a kind of analogical cognition and way of thinking of people. In recent years, metaphors have made more and more progress in Chinese language recognition, so their status has gradually risen. Chinese language sentiment analysis is a relatively difficult problem in language processing. Chinese language semantics is ambiguous and complex, so it is a great challenge to construct Chinese Emotion structure. In this paper, Chinese nouns and Chinese verbs are used as test samples to conduct metaphor test calculation. The test results of mutual information and information entropy show that for the sample metaphor calculation, the correct rate of individual words is less than 70%. The ratio between the macro-average of gerund words and the baseline fluctuates relatively large. The metaphor recognition of nouns is about 20% higher than the original test results and about 10% higher for verbs. It shows that under the action of mutual information and information entropy, the metaphor recognition performance of the basic structure of Chinese Emotion constructed from the perspective of metaphor is improved, and further illustrates the feasibility of mutual information and information entropy algorithm for building the basic structure.

Language in people with cervical dystonia: Evidence of grammatical and specific semantic deficits

BackgroundAlthough historically considered a motor disorder, cervical dystonia (CD) may present with subtle cognitive impairments. Basal ganglia dysfunction in other neurological conditions can lead to language impairments. Language in people with CD (pwCD) remains unexplored. ObjectivesThe study aimed to explore phonological, grammatical, and semantic language abilities in pwCD compared to healthy controls. Methods19 pwCD and 20 control participants completed the Object and Colour subtests of the Rapid Automized Naming Task (RAN), the Test for Reception of Grammar-2 (TROG-2), and a lexical decision task with a masked priming paradigm that compared reaction times to words varying according to two factors-hand relatedness (hand-related, non-hand-related) and word category (verb, noun). ResultsCompared to controls, pwCD were less accurate at grammatical comprehension on the TROG-2 (p < 0.05, n2 = 0.15). There were no significant differences between pwCD and controls in phonological retrieval, as measured by the RAN. PwCD demonstrated an overall reduced priming effect for all words, however, there is some evidence in our data that this may be more pronounced for hand-related words. ConclusionLanguage deficits should be considered an area of future research in pwCD. These findings support the role of the motor system in language.

Research of semantic aspects of the Kazakh language when translating into the Kazakh sign language

The article discusses the semantic aspects of Kazakh sign language and its characteristics. Semantics, a field within linguistics, focuses on examining the meanings conveyed by expressions and combinations of signs. The author delves into the inquiry of the degree of similarity between verbal and sign languages, highlighting their fundamental distinctions. The primary objective of the research is to scrutinize the characteristics of parts of speech in the Kazakh language when expressed gesturally, along with the principles governing the translation of verbs and adverbial tenses. The article explains in detail the formulas for translating the text into sign language, based on the subject-object-predicate. Examples are given that illustrate the subject-object relationship and determine who acts as the speaker, "object" or "subject" of the utterance. It is necessary to note that for successful translation it is necessary first to understand the meaning of the sentence. The article concludes by emphasizing the importance of understanding both structural elements and contextual nuances in the fascinating world of the semantics of the Kazakh sign language. It inspires further research aimed at uncovering the complexities and exceptions that contribute to a deep understanding of linguistic nuances in this unique form of communication.

Understanding Contextual Aspects of the Word ‘Look’ in Corpus of Contemporary American English: Semantics Study

This research investigates the contextual aspects and lexical semantics of the word "look" within the Corpus of Contemporary American English (COCA). By examining how "look" functions across different contexts, this study aims to uncover the multifaceted nature of its meaning and usage. The research is grounded in semantic theory, drawing on the works of Leech, and other prominent linguists to explore how meaning is constructed and conveyed through language. Key focuses include the categorization of "look" across various parts of speech, such as verbs, nouns, and interjections, and the examination of its idiomatic, non-idiomatic, and compositional meanings. The study employs a qualitative research methodology, analyzing linguistic data from COCA to identify patterns and themes in the usage of "look." This research aims to elucidate the complex interplay between context and meaning, offering insights into the cognitive processes involved in language comprehension and production. The findings highlight the significance of semantics in understanding language, particularly how specific words can convey a wide range of meanings depending on their contextual usage. Overall, this research contributes to the field of lexical semantics by providing a comprehensive analysis of the word "look," demonstrating its versatility and the intricate mechanisms by which meaning is negotiated in language. The research has implications for linguistic theory, language education, and the development of more precise language processing systems.