Frame Semantics Research Articles

A Clarified Examination of the Item Wording Effect: Item Valence (Good vs. Bad) Versus Semantic Framing (I Am vs. I Am Not).

The Item Wording Effect (IWE) in psychological testing describes how individuals respond differently to positively and negatively worded items. Previous IWE research faced challenges due to measures varying beyond item valence. This study aimed to address this problem by developing an inventory, the Positive and Negative Descriptor Inventory (PANDI), with items varying solely on valence. Semantic framing was manipulated to examine which factor (valence vs. framing) was more causal of the IWE. Using an online survey on Mechanical Turk, 336 Canadian participants responded to PANDI items in different experimental conditions. Results indicated that item valence had a bigger impact on IWE than semantic framing. PANDI-Good items in the Affirming Condition exhibited lower reliability but higher means and response variance than other groups, emphasizing the significant difference in how individuals interpret positive and negating inventory items. This study recommends using negatively worded items sparingly, and not using negating items at all.

Transfer learning model for anomalous event recognition in big video data.

Video surveillance faces challenges due to the need for improved anomalous event recognition techniques for human activity recognition. Growing security concerns make standard CCTV systems insufficient because of high monitoring costs and operator exhaustion. Therefore, automated security systems with real-time event recognition are essential. This research introduces a semantic key frame extraction algorithm based on action recognition to minimize frame volume big video data. This approach has not been previously applied with ResNet50, VGG19, EfficientNetB7, and ViT_b16 models for recognizing anomalous events in surveillance videos. The findings demonstrate the effectiveness of this method in achieving high accuracy rates. The proposed method addresses the challenges posed by large volumes of frames generated by surveillance videos, requiring effective processing techniques. A large number of videos from the UCF-Crime dataset were used for proposed model evaluation, including both abnormal and normal videos during the training and testing phase. EfficientNetB7 achieved 86.34% accuracy, VGG19 reached 87.90%, ResNet50 attained 90.46%, and ViT_b16 excelled with 95.87% accuracy. Compared to state-of-the-art models from other studies, the transformer model (ViT_b16) outperformed these algorithms, demonstrating significant improvements in recognizing anomalous events.

The “Weird” Effect: Shaping U.S. Election Rhetoric

In 2024, as the U.S. election draws close, the media partition bias is increasingly obtrusive in its media framing. This essay explores the broader implications of this rhetorical shift by first analyzing the introduction of “weird” in the 2024 U.S. election discourse with Tim Walz’s viral comment and then comparing the term’s usage by MSNBC and Fox News to explore the partisan usage of the term and how they influence public perception, weaponize humor, and construct biased narratives. The comparisons will center around how semantic framing of words like “weird” creates effective negative priming for the audience and strengthens emotional tendencies such as cue-taking or confirmation bias. Finally, the essay discusses the broader implications of this rhetorical shift, embedding political battles into everyday life and complicating American political discourse. While not new in strategy, the essay underscores how this linguistic tactic uniquely infiltrates common language, reshaping political engagement in contemporary America and further polarizing American society.

Semantic-aware frame-event fusion based pattern recognition via large vision–language models

Pattern recognition through the fusion of RGB frames and Event streams has emerged as a novel research area in recent years. Current methods typically employ backbone networks to individually extract the features of RGB frames and event streams, and subsequently fuse these features for pattern recognition. However, we posit that these methods may suffer from two key issues: (1). They attempt to directly learn a mapping from the input vision modality to the semantic labels. This approach often leads to sub-optimal results due to the disparity between the input and semantic labels; (2). They utilize small-scale backbone networks for the extraction of RGB and Event input features, thus these models fail to harness the recent performance advancements of large-scale visual-language models. In this study, we introduce a novel pattern recognition framework that consolidates the semantic labels, RGB frames, and event streams, leveraging pre-trained large-scale vision–language models. Specifically, given the input RGB frames, event streams, and all the predefined semantic labels, we employ a pre-trained large-scale vision model (CLIP vision encoder) to extract the RGB and event features. To handle the semantic labels, we initially convert them into language descriptions through prompt engineering and polish using ChatGPT, and then obtain the semantic features using the pre-trained large-scale language model (CLIP text encoder). Subsequently, we integrate the RGB/Event features and semantic features using multimodal Transformer networks. The resulting frame and event tokens are further amplified using self-attention layers. Concurrently, we propose to enhance the interactions between text tokens and RGB/Event tokens via cross-attention. Finally, we consolidate all three modalities using self-attention and feed-forward layers for recognition. Comprehensive experiments on the HARDVS and PokerEvent datasets fully substantiate the efficacy of our proposed SAFE model. The source code has been released at https://github.com/Event-AHU/SAFE_LargeVLM.

Five Models Used in Contrastive Semantics: A Comprehensive Synthesis and Analysis

The present study aims to assess and contrast five different approaches in the field of contrastive semantics: natural semantic metalanguage (NSM), conceptual metaphor theory (CMT), frame semantics (FC), semantic field theory (SFT), and cognitive grammar (CG). The research is set to review the available literature on the described techniques in order to focus on their distinguishing characteristics, practical applications, and illustrative samples. The findings reveal that these five models do play an insightful role when it comes to analyzing and comparing synonymous and semantically related profiles across distinct languages. Relations of semantic elements are the core concern of NSM while CMT investigates the internal schemata of the mind. As opposed to cognitive frame analysis, where knowledge structures are the central object of study, in SFT are language units grouped according to their meaning, while CG sees no separation of grammatical and meaning structures. This paper expounds on how these frameworks fill the gaps left by each of them and offers a broader understanding of the ways words encode meaning and how speakers of different cultures think about the world. Moreover, this study augments the knowledge regarding the variations of languages and gives an understanding of how languages relate to cognition, culture, and how people think about the world through words.

Extracting Semantic Frames from Specialized Corpora for Lexicographic Purposes

This paper focuses on Frame-based Terminology (FBT), a theoretical framework based on cognitive premises that underpin the representation of specialized objects and events. It demonstrates that specialized semantic frames can be inferred from lexical schemas and semantic annotation. This research used corpus techniques to collect data for the representation of multilingual semantic frames. The research focuses on deforestation within the domain of Environmental Science. The methodology involves the encapsulation of corpus queries and semantic annotation to guide the creation of specialized frames associated with the deforestation event. Although the creation of frame-based terminological resources can be time-consuming, our results show that it is facilitated by the use, of e-tools that extract the arguments of specialized predicates, which are often immersed in complex syntactic constructions.

Figurativeness of the Japanese flag

Abstract The analysis offered in this article applies the multilevel approach to conceptual metaphor to relief posters, created and distributed widely after the massive Japan 2011 earthquake. The aim of the analysis is to show in what way the multilevel approach to metaphor can be used to illustrate how the figurative meaning of relief posters emerges from the interactions between different levels of schematicity: from basic image schemas and domains, through contextually embedded semantic frames, finishing with metaphorical scenarios at the highest level of specificity and complexity. The present analysis also shows the potential applicability of image schemas as the guiding analytical element, serving as the departure point for the unfolding metaphorical and metonymical interpretation of a multimodal message. Accordingly, it has been shown how the image schemas of part-whole, full-empty, process and force aid the analysis of metaphoricity and how the central metonymic element of the national flag co-constructs the figurative reading of the posters.

Semantic Analysis of Selected Nigerian Newspaper Reports on the Effect of the 2023 Naira Redesign

This study is a semantic analysis of news reports on the effect of the 2023 Nigerian Naira redesign from selected Nigerian newspapers. A total of 40 news reports were purposively sampled from four Nigerian newspapers (The Sun, Vanguard, The Guardian and Premium Times). Fillmore’s (1982) Frame Semantic Theory, which proposed that language is composed of semantic frames, which are mental representations of situations or scenarios, was adopted for this study. A qualitative research design was employed in the data analysis to examine the elements of Frame Semantic Theory evident in the extracted data. The findings revealed that all the four newspapers consistently framed the effects of the Naira redesign within the context of economic hardship. It also revealed that the newspapers predominantly conveyed negative perspectives regarding the Naira redesign. The cross-newspaper comparative analysis also highlighted the widespread and varied effects of the Naira redesign.

The road ahead for Construction Grammar

Abstract What does the future hold for Construction Grammar? What are the most promising future avenues for research on constructions? This paper addresses the development of Construction Grammar as a theory of language through the perspective of six recent PhD dissertations that explore constructional meaning, the architecture of the constructional network, and the role of language change in a constructional theory of language. The goal of this paper is to establish connections between these ideas, and to spell out how different questions concerning Frame Semantics, distributional semantic methods, priming, nodes and connections, individual differences, and constructional change all contribute to a picture that is bigger than the sum of its parts.

Event-Oriented State Alignment Network for Weakly Supervised Temporal Language Grounding.

Weakly supervised temporal language grounding (TLG) aims to locate events in untrimmed videos based on natural language queries without temporal annotations, necessitating a deep understanding of semantic context across both video and text modalities. Existing methods often focus on simple correlations between query phrases and isolated video segments, neglecting the event-oriented semantic coherence and consistency required for accurate temporal grounding. This can lead to misleading results due to partial frame correlations. To address these limitations, we propose the Event-oriented State Alignment Network (ESAN), which constructs "start-event-end" semantic state sets for both textual and video data. ESAN employs relative entropy for cross-modal alignment through knowledge distillation from pre-trained large models, thereby enhancing semantic coherence within each modality and ensuring consistency across modalities. Our approach leverages vision-language models to extract static frame semantics and large language models to capture dynamic semantic changes, facilitating a more comprehensive understanding of events. Experiments conducted on two benchmark datasets demonstrate that ESAN significantly outperforms existing methods. By reducing false high correlations and improving the overall performance, our method effectively addresses the challenges posed by previous approaches. These advancements highlight the potential of ESAN to improve the precision and reliability of temporal language grounding tasks.

Knowledge structures for knowledge communication: Dominant semantic frames in research articles

Taking Frame Semantics as its theoretical framework, this corpus-based study addresses a lack of research on semantic knowledge in research articles at the genre level, as opposed to the discipline level, by identifying the dominant Semantic Frames in the academic portion of the Corpus of Contemporary American English. Semantic Frames in the corpus were first automatically identified with a pre-trained large language model fine-tuned with data from FrameNet, a large database built on the principles of Frame Semantics. Subsequently, 84 dominant Semantic Frames in research articles were extracted based on the frequency distributions of identified semantic frames, constituting a Research Article-specific Semantic Frame List. Based on their definitions and detailed analyses of typical example sentences, these 84 Research Article-specific Semantic Frames were classified into three groups: Academic Activity Frames, which describe typical activities for creating knowledge, Academic Communication Frames, which relate to typical ways for disseminating knowledge, and Academic Interest Frames, which include typical entities or attributes that interest researchers across disciplines. The Research Article-specific Semantic Frame List sheds light on the semantic knowledge that is characteristic of the research article genre and shared across disciplines, thereby paving the way for compiling a research article-specific framenet. Further research is encouraged to explore the Frame Element distribution patterns of Research Article-specific Semantic Frames and Semantic Frames specific to other academic genres.

Terminology, popularisation and ideology in contemporary China

Abstract This paper explores the relationship between terminology, the popularisation of scientific and technical knowledge and ideology in the People’s Republic of China. Specifically, it presents a study that focuses on an online daily column entitled ‘Scientific & Technical Term of the Day’ (每日科技名词), a terminology popularisation project promoted by China’s contemporary political leadership and implemented through the digital institutional platform Xuexi qiangguo 学习强国. Drawing on the theoretical frameworks of critical discourse studies and legitimation in discourse, as well as frame analysis and frame semantics, the paper presents an analysis of expository texts designed to complement the knowledge provided by terminographic definitions of the terms published in the daily column. The focus is on the framing of China and the Party-state in these texts, which, it is argued, can also be interpreted as legitimation strategies.

The view of meaning from a “postclassical” perspective

Abstract In recent years, a number of scholars have expressed doubts about the productivity of the concept of meaning and its associated methodology for modern lexical semantics. This article aims to examine the current situation by comparing it with the process of transition from classical to quantum physics. Empirical data that challenge classical interpretations are briefly analyzed in a special section, whilst the subsequent sections address alternative theories that propose new methodological frameworks. Particular attention is paid to the ad hoc СС & Ms theory developed by Daniel Casasanto and colleagues, though Hans-Jörg Schmid’s Entrenchment-and-Conventionalization Model and the Motivation & Sedimentation Model formulated by Jordan Zlatev and colleagues are also touched upon. In the final section, frame semantics, as presented by Charles Fillmore, is revisited, with a focus on his dichotomy of U-semantics and T-semantics. A significant result of the analysis of Fillmore’s perspectives is the assertion that the concept of frame in Fillmore’s construal can serve as an alternative to the concept of meaning in its classical interpretation.

Exploration of Semantic Frames to Confirm Verb Meaning

Semantics is one of three levels of language analysis: phonological, grammatical, and semantic. Semantics is a field of linguistics that studies the relationship between linguistic signs and the objects they signify, or in other words, a field of linguistic study that investigates what language means and means. Semantics is a field of linguistics that is closely related to other social sciences, such as sociology or anthropology, and even to philosophy and psychology. Sociology is related to semantics due to the fact that the identity of social groups can be demonstrated by the use of certain words to indicate meaning.

Reflexivity patterns in West-Slavic languages

Abstract The aim of this paper is to analyse differences in reflexive coding in Slovak, Czech and Polish and to evaluate the factors responsible for using phonologically more or less complex reflexive markers. To address this issue, we looked at preferred reflexive coding strategies in Slovak, Czech, and Polish, relying on data extracted from InterCorp multilingual corpus. The results are then verified by data from monolingual corpora of investigated languages for one semantic group of verbs labelled as ‘Prevarication’ in FrameNet. The results show that semantic frame underlying the meaning of lexical items cannot be the only possible explanation for distribution of reflexive markers but there are also other semantic, syntactic and pragmatic factors playing a pivotal role in reflexive coding strategies, often unique for a given language.

Repetition and variation in a Finnish music-related discourse: A case study

Abstract Among the different ways to approach discourse-structuring patterns, semantic basic figures (Grundfiguren in German) are a versatile tool that helps to understand the semantic framework of discourses. Topoi, metaphors, and other argumentative and figurative elements can be traced back to such basic concepts. My essay identifies and analyses repetitions and variations in the realisation of two such figures – ‘difficulty’ and ‘austerity’ – in a corpus of concert reviews of Jean Sibelius’ Fourth Symphony. I show how these concepts are first established and how they can be found in both verbatim repetitions and lexical and constructional variations over a period spanning several decades. My approach is based on the observation that a constantly varied repetition of certain patterns and similarly structured semantic frames is effective in coining the image of an artwork in public discourse and reaching an intersubjective understanding of its meaning and content. This applies all the more when those patterns are linked to previously established cultural (auto-)stereotypes.

Linguistic analysis of fiction texts in the context of the frame theory

The aim of the study is to identify and analyse frame structures, activators, and concepts in texts to reveal their impact on the reader' s perception. The research employed the methods of semantic, structural, and intertextual frame analysis. Cronbach' s alpha was also used to verify the instruments used. The following cases were used in the work: Oxford English Corpus and International Corpus of English (ICE). It is noted that frame activators such as "great heat", "hell heat", "hot wind", and "scorching sun" evoke associations with scorching heat and unusual warmth in the desert. These frames convey the impression of life danger, exhaustion, and severity of conditions. In turn, phraseological units such as "fresh waters", "bloody sunset", and "dried earth" reveal the contradictory nature of the image of the desert, where even such natural phenomena as water and sunset acquire a new, deep shade of meaning. The application of this theory was found to reveal complex language structures and their influence on the understanding and perception of texts. The obtained results open up new opportunities for educational and literary analysis, deepening the understanding of language mechanisms in fiction. Further studies in the field of linguistic analysis of texts should be aimed at considering the interaction between different works of art, focusing on the frame perspective. It is also worth paying attention to the possibilities of using the theory of frames to fulfill practical assignments, such as automatic text analysis.

SEMANTIC CONTENT OF CONCEPT HELP CATEGORIZED AS PROPERTY IN CONTEMPORARY ENGLISH

The article raises the issue of conceptual-semantic analysis of units of different lexical-grammatical classes to indicate rivalry. The referent of objective reality represented by the RIVALRY concept is a certain situation as a set of its interdependent elements. The analysis of the semantic structure of nominative units contributes to the establishment of deep patterns of interaction between the structures of human consciousness and linguistic forms. Nominative possibilities of the linguistic picture of the world are implemented by lexical units that are subject to conceptual analysis to achieve the goal of linguistic-cognitive research - reconstruction of the content of the concept. Dictionary definitions of words of different lexical-grammatical classes to indicate rivalry contain generic and specific semes as lexical units that implement the corresponding cognitive characteristics and are called slot terminals. The frame acts as a classifier of lexical units in which the components of the reference situation of rivalry are actualized. The semantic-cognitive analysis of lexemes that form the primary and secondary synonymous series and most adequately reflect the studied conceptual category precedes the modeling of the prototypical informative structure of the concept of rivalry in terms of frame semantics. Slot ACTION, which captures information about the actions of direct participants, is represented by units of the lexical-grammatical category to indicate the rivalry process. The studied slots reflect knowledge about the causal, instrumental, location and time components of the frame model. The studied slots are correlated with a group of cognitive features that represent knowledge about the characteristics of actions.Thus, the causal set activates data on the reasons for entering into a rival interaction, options for ending it. The instrumental set accumulates information about the nature of the action, tactics, strategic goal; the quantity and quality of the means of action, about the quantitative and qualitative aspects of the goal, its varieties. The location and time of the frame captures knowledge about the type of the place and spheres of action, the duration of the rivalry and the time of its implementation.

The Red Hen Audio Tagger

Abstract The International Distributed Little Red Hen Lab, usually called “Red Hen Lab” or just “Red Hen”, is dedicated to research into multimodal communication. In this article, we introduce the Red Hen Audio Tagger (RHAT), a novel, publicly available open source platform developed by Red Hen Lab. RHAT employs deep learning models to tag audio elements frame by frame, generating metadata tags that can be utilized in various data formats for analysis. RHAT seamlessly integrates with widely used linguistic research tools like ELAN: the researcher can use RHAT to tag audio content automatically and display those tags alongside other ELAN annotation tiers. RHAT additionally complements existing Red Hen pipelines devoted to natural language processing, speech-to-text processing, body pose analysis, optical character recognition, named entity recognition, computer vision, semantic frame recognition, and so on. These cooperating Red Hen pipelines are research tools to advance the science of multimodal communication.

Voices of rape: Cognitive networks link passive voice usage to psychological distress in online narratives

Past studies of sexual assault have found that passive voice descriptions of rape elicit an increased perception of victim responsibility compared to active voice narratives (Bohner, 2001), contributing to victim blaming and the perpetuation of rape myths. Building on this, we investigate the relationship between passive/active voice usage and perception, but from the perspective of rape survivors as disclosed in their online rape narratives. We collect rape narratives from Reddit's r/sexualassault board and group them into a passive voice group and an active voice group. We detect differences between the two groups of text using a cognitive network science approach that creates network representations from text such that nodes represent words/concepts while links represent syntactic and semantic relationships between them. We systematically identify nodes that are significantly more central to one network compared to the other, thus identifying characteristic concepts that semantically differentiate the two groups of narratives. We then investigate the contexts of these concepts applying semantic frame analysis. We find that concepts related to psychological distress (e.g. PTSD, flashback) are significantly more central to passive voice narratives, providing quantitative evidence of a link between passive voice usage and an increased focus on psychological distress. We also find that family members (e.g. parent, brother) are more central to active voice narratives, suggesting a connection between active voice usage and an increased focus on others' roles in rape survivors' experiences. Our quantitative results reveal an important link between language and mental health that has valuable implications for therapeutic interventions.