Frame Semantics Research Articles

Toward a dynamic frame-based ontology of legal terminology

In the study of special languages and translation, the legal field is often insulated from other domains. This is primarily due to the extreme system dependence of the terminology of law, which results from a lack of a common legal system of reference throughout the world. The abstract nature of this human-made field and its dynamicity in view of the continuously evolving case law and constant changes in legislation make it difficult to illustrate its complex ontology through traditional terminology management techniques. Therefore, this paper argues for an interdisciplinary approach to constructing the ontology of legal concepts based on structural constituents from frame semantics and comparative law. Frames allowing for the representation of interconnected knowledge segments evoked by legal concepts and the distinction between micro- and macro-dimensions in legal comparison research make it possible to capture the complex ontology of legal terminology evoked in a specific point in time and a determined legal context. The ontological knowledge structure will be exemplified by terms from German social, commercial, employment, and tax law.

A Taste of Metaphor Variation: Contrasting the Metaphorical Extensions of “stew” and “guisar”

This article examines the range of metaphors activated by the cooking term stew in English and its Spanish counterpart guisar. The data were drawn from Corpus of Contemporary American English (COCA) and Corpus del Español: Web and Dialects. The metaphors were identified by applying MIP (Pragglejaz Group, 2007) and incorporating semantic frames (Fillmore, 1982) to provide a detailed analysis of the mappings between the core frame elements involved, and the thematic roles performed by those elements. The results suggest that English and Spanish diverge considerably regarding the metaphors evoked by stew and guisar. The lemma stew activated a broader scope of metaphorical extensions than guisar. Just one shared metaphor was identified, which underscores the presence of distinct cognitive preferences within the respective languages.

Terminology in the domain of seafood: A comparative analysis Germany-Spain

In the last few decades, the study of terminology has undergone a cognitive shift that has led to the development of several approaches that study the social, linguistic, and cognitive dimension of terms, such as Communicative Theory of Terminology (CTT) and Frame-Based Terminology (FBT). CTT was developed in the early 1990s and argues that the study of terminology should be based on a communicative perspective, taking into account aspects such as the communicators and the context of communication. FBT has been developed from 2007 and uses certain aspects of Frame Semantics to conceptualise specialised domains and create non-language-specific representations through the analysis of the domain event and on the study of the behaviour of the terminological units in texts. The two theories share many of the same premises and propose the representation of the concepts of a domain in an ontology. FBT also proposes a representation in frames. We explore how these two methods of domain representation can be used to represent the terminology of the domain of seafood in Germany and Spain.

Ruled by construal?

Abstract In cognitive linguistics, grammatical structure is known to be representative of meaning. This is also true of English articles. In this paper, we argue that the choice of article, when the grammar allows it, is dependent on the wider discourse context and most importantly on how the speaker construes this context. Using survey data from 181 native speakers of English, we show that the choice of article depends on the activation of semantic frames and how speakers may choose to highlight different elements of a frame to construe the situation differently. We rely on Entropy to measure the restrictiveness of a context and to identify particular contexts in which choice is allowed or inhibited. We find that some contextual features such as the specificity of the referent are more restrictive while Hearer Knowledge is more open to construal.

Actual Problems of Cognitive Linguistics at the Present Stage

This article is devoted to the study of the historical and scientific prerequisites for the formation of a young, developing, and at the same time controversial and controversial scientific branch of linguistics - cognitive linguistics. The goals, object and subject of research in this field of knowledge are determined by theoretical and methodological principles that are formed on the basis of the cognitive-oriented work of Kazakh and foreign linguists. The continuation of the research traditions of cognitive linguistics, which is gaining popularity among Kazakh researchers, is noted. The theoretical and scientific basis of the research are such linguistic areas as frame and conceptual semantics, prototypical semantics, the theory of relevance (or linguistic emphasis), the theory of cognitive models and cognitive metaphor, the theory of mental spaces. Special attention is paid to the problems of the English language in comparison with other language groups, which creates difficulties in linguists relying on traditional approaches to the study of the language system. The identified specific features in the use of English prepositions, modal verbs, the use of participles, the category of voice, tense, aspect, mood, as well as some difficulties in coordinating tenses lead researchers to the need to use linguo-cognitive methods of cognition. The establishment of a close and complex relationship between language and the process of thinking, cognition is accepted as an evidence-based advantage of the study. The data obtained as a result of the analysis are based on logical, deductive, comparative linguistic research methods. The tools used to assess the current problems of modern cognitive linguistics can be of applied significance and be used in international scientific communities to improve approaches to language learning. The scientific and practical significance of this study lies in the search for possible ways and implementation of identification opportunities, as well as a comprehensive study of the most pressing problems, reflecting the urgent need for further development of this scientific field.

Frame Semantic Role Labeling Using Arbitrary-Order Conditional Random Fields

This paper presents an approach to frame semantic role labeling (FSRL), a task in natural language processing that identifies semantic roles within a text following the theory of frame semantics. Unlike previous approaches which do not adequately model correlations and interactions amongst arguments, we propose arbitrary-order conditional random fields (CRFs) that are capable of modeling full interaction amongst an arbitrary number of arguments of a given predicate. To achieve tractable representation and inference, we apply canonical polyadic decomposition to the arbitrary-order factor in our proposed CRF and utilize mean-field variational inference for approximate inference. We further unfold our iterative inference procedure into a recurrent neural network that is connected to our neural encoder and scorer, enabling end-to-end training and inference. Finally, we also improve our model with several techniques such as span-based scoring and decoding. Our experiments show that our approach achieves state-of-the-art performance in FSRL.

Study on C-E Translation of Dish Names in Shandong Cuisine from Perspective of Frame Semantics

Shandong cuisine plays a crucial role in promoting its international presence as an important component of Chinese culinary culture, serving as a significant avenue for spreading traditional Chinese culinary culture. This paper analyzes the English translation of Lu cuisine dish names from the perspective of frame semantics, exploring the frame relationships between the original Chinese names and their English translations. Subsequently, based on frame semantics, the paper propositionally puts forward several standardized English translation strategies for Lu cuisine dish names. These strategies aim to ensure that the English translations not only conform to the standards of the English language but also effectively convey the unique culinary culture of Shandong.

In search of an optimal method for analyzing deep structures: frame semantics and classification of argumentative structures

The subject of the research is the search for the optimal method of analyzing deep structures using frame semantics. The study of semantic roles, similarities and differences in the approaches of both C. Fillmore and B. Levin – M. R. Hovav make it possible to analyze the structure of a sentence in more detail and accurately, identify deep cases and determine semantic relations between words. The study of these aspects is key to understanding language constructs and their interpretation. The study of various approaches makes it possible to identify both common features and unique features, which is key for a complete understanding of language constructions. An interest in text analysis in the field of artificial intelligence, machine learning and computational linguistics, and an understanding of the semantic relationships between words will help create more accurate and efficient text processing algorithms. One of the research methods is the semantic analysis of sentences based on corpus data. This method includes the study of various linguistic constructions in the context of their use in real texts, which allows us to identify common patterns and rules for the use of these linguistic units in different situations. The scientific novelty of the study lies in the fact that the authors have determined the similarity of the approaches on how to understand the surface and deep structures of language of Ch. Fillmore and B. Levin and M. Rappaport. Their work, despite differences in methodology and terminology, together allow for in-depth investigation of the relationship between the meanings of verbs and the structure of arguments. As a result of the study, the natural relationships between deep cases and semantic roles in sentences of various types are revealed, and key points that need to be taken into account when analyzing deep structures for a more accurate definition of the semantic roles of arguments are highlighted: frame semantics and thematic grids. Disagreements and alternative points of view contribute to the constant development and improvement of linguistic theories. Such debates eventually lead to a deeper understanding of the implementation of the arguments and open up opportunities for further research in this area. Both C. Fillmore and B. Levin and M. Rappaport have made significant contributions to understanding the surface and deep structures of language, although their approaches and terminology may differ.

Model of Artifi cial Intelligence for Forming the Federal Reserve as a Driver for Digitalization of the Economy

The article discusses theoretical issues regarding the personnel reserve, the formation of the "Random Forest" DL model for the selection of applicants for the Russian federal reserve in modern conditions. The relevance of the study lies in the fact that in modern conditions artifi cial intelligence systems based on cognitive modeling, the use of artifi cial intelligence and the formation of digital ecosystems are increasingly being used. The scientific novelty lies in the fact that in the study conducted, a deep learning model DL-model "Random Forest" was formed, which allows the selection of applicants when forming the federal reserve. The practical significance of the study is that the results obtained can be recommended for implementation in practice to provide support for management decisions in the formation of the federal reserve. The cognitive model was developed in the GraphViz environment using a semantic frame network in the form of graphs in the DOT programming language. A questionnaire was developed to identify the qualitative characteristics of applicants, which made it possible to create a "Random Forest" DLmodel dataset. The criterion for the success of the predictive properties of the DL model was the value of the average forecast error (MAE). The proposed DL model uses the best decision tree, which has optimal hyperparameter settings, for example, the depth of the tree is six layers, the number of estimators (trees) in the ensemble is ten. In the experiment, the hyperparameters of the neural network did not change; the input parameters to various trees were selected randomly by the algorithm.

Innovative Digital Model of the Infl uence of the Key Rate on the Profi tability of the Russian Banking System

The article considers theoretical approaches to modeling the sustainable development of the Russian banking system in the context of innovative transformations and the formation of digital ecosystems. The relevance of the study lies in the fact that in modern conditions, approaches are increasingly being used to ensure the sustainable development of the banking system based on cognitive modeling, the use of artifi cial intelligence and the formation of digital ecosystems. The scientific novelty lies in the fact that in the study, a deep learning model DL-model "Random Forest" was formed, which allows you to get a stable forecast for the net profi t of the banking system, in the conditions of innovative transformations and the formation of digital ecosystems. The practical signifi cance of the study is that the results obtained can be recommended for implementation in practice to provide support for managerial decision-making regarding the sustainable development of the banking system. The cognitive model was developed in the GraphViz environment using a semantic frame network in the form of graphs in the DOT programming language. An analysis of the dynamics of both macroeconomic indicators of the real sector of the economy and the parameters of the development of the banking sector of the Russian Federation was carried out. The criterion for the success of the predictive properties of the DL-model was the value of the average forecast error (MAE). The proposed DL model uses the best decision tree that has optimal hyperparameter settings, for example, the depth of the tree is six layers, the number of estimators (trees) in the ensemble is ten. In the experiment, the hyperparameters of the neural network did not change, the input parameters to various trees were randomly selected by the algorithm. The DL model showed high forecast accuracy.

An Analysis on English Teachers’ Effective Classroom Discourse and Its Interactive Model Innovation with the Assistance of Artificial Intelligence

Abstract The rapid evolution of technology catalyzes innovation in English teaching methodologies, rendering traditional approaches—predominantly reliant on singular language modalities—insufficient for modern educational demands. This paper introduces a multimodal English teaching model anchored in multimodal discourse analysis and interaction theory to address these evolving needs. Utilizing a dual threshold extraction algorithm for chunked cumulative frame difference, the model effectively identifies semantic key frames during multimodal English instruction. Additionally, it incorporates a composite convolutional neural network that merges spatio-temporal features to delineate the dynamics of the teaching videos, further enhanced by the LK optical flow method. The integration of a pre-interactive LSTM decoder facilitates the fusion of video and textual features, culminating in the construction of an annotated English teaching video model. This model was applied to analyze the classroom discourse of high school sophomores’ English teachers in X city. Analysis revealed that the duration of classroom discourse varied between 14 to 19 minutes per session, with the introductory segment alone accounting for 307.54 seconds—12.81% of the total lesson duration—of purely linguistic content. Furthermore, the average number of questions posed by teachers per lesson was 73.17, indicating a high reliance on verifying discourse strategies, which constituted 92.22% of the discourse. This technological approach to analyzing classroom discourse provides novel insights and valuable references for refining English teaching strategies, demonstrating the effectiveness of integrating advanced computational techniques in educational settings.

Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations

Text-Video Retrieval plays an important role in multi-modal understanding and has attracted increasing attention in recent years. Most existing methods focus on constructing contrastive pairs between whole videos and complete caption sentences, while overlooking fine-grained cross-modal relationships, e.g., clip-phrase or frame-word. In this paper, we propose a novel method, named Hierarchical Cross-Modal Interaction (HCMI), to explore multi-level cross-modal relationships among video-sentence, clip-phrase, and frame-word for text-video retrieval. Considering intrinsic semantic frame relations, HCMI performs self-attention to explore frame-level correlations and adaptively cluster correlated frames into clip-level and video-level representations. In this way, HCMI constructs multi-level video representations for frame-clip-video granularities to capture fine-grained video content, and multi-level text representations at word-phrase-sentence granularities for the text modality. With multi-level representations for video and text, hierarchical contrastive learning is designed to explore fine-grained cross-modal relationships, i.e., frame-word, clip-phrase, and video-sentence, which enables HCMI to achieve a comprehensive semantic comparison between video and text modalities. Further boosted by adaptive label denoising and marginal sample enhancement, HCMI achieves new state-of-the-art results on various benchmarks, e.g., Rank@1 of 55.0%, 58.2%, 29.7%, 52.1%, and 57.3% on MSR-VTT, MSVD, LSMDC, DiDemo, and ActivityNet, respectively.

A Study on the International Communication of Traditional Chinese Medicine Culture from the Perspective of Frame Semantics

A Study on the International Communication of Traditional Chinese Medicine Culture from the Perspective of Frame Semantics

The Semantic Frame of The Royal Funeral

Any form of life ends in death. Human death is a difficult moment for an individual’s family. Various cultures observe diverse rituals and traditions connected with the end of life and perform different burial rites. Memorial services assume a range of forms depending on the culture and religious tradition an individual was brought up in and followed throughout their life. However, memorial ceremonies are also diversified within a given culture. The funeral of a monarch is much more complex as compared to this of any of his/her subject’s. The death of the British Monarch, Queen Elizabeth II, in September 2022 was followed by a period of mourning and state funeral. The analysis in the present paper focuses on the semantic frame of the royal funeral. The methodological framework adopted for the discussion is the cognitive linguistic one, focusing on cognitive-linguistic models such as frames and scripts, exemplified here by the frame of the ROYAL FUNERAL and the [ROYAL FUNERAL] script. Both of the presented models contain unique elements that are not to be found in other contexts. The components of frame and script which are specific to this event are highlighted. The data for the detailed description of the frame are gathered from the online news reports provided by the British Broadcasting Corporation. The study shows how different the discussed model is from the standard description of the frame of FUNERAL and the [FUNERAL] script respectively. Also, many of its elements are culture-specific.

Making Sense of Destruction: A Frame-Semantic Analysis of šḥt in the Hebrew Bible

The verb šḥt is worthy of investigation. It is a prominent verb of destruction that occurs throughout the Hebrew Bible. It is also polysemous. Furthermore, its basic meaning has not yet been determined in the various reference works and it defies the traditional wisdom that specific stems have specific meanings. Great advances have been made in utilising cognitive linguistic (CL) methodologies to interpret the Hebrew Bible (HB). We are not aware of any robust attempt at using insights from CL to investigate the meaning of šḥt. For this study, we utilised frame semantics (FS) and several other CL methodologies to gain insight into the semantic force of šḥt in the HB.

The Linguistic Characteristics of Perception Verbs in Conveying Buddhist Teachings

This study aims to analyze the linguistic characteristics of perception verbs used in conveying Buddhist teachings by using Lakoff and Johnson's (1980) conceptual metaphor theory and Fillmore's (1982) frame semantics as the theoretical foundation, this study examines the language properties of perception verbs employed in Buddhist teachings. Concrete, corporeal experiences conveyed metaphorically by perception verbs enable the understanding of abstract spiritual notions through conceptual metaphors. Frame semantics highlights how the cultural background and contextual backdrop surrounding Buddhist speech inform the rich semantic meaning of these verbs. Utilizing a qualitative methodology, the study applies content analysis and NVivo 14 software to examine a corpus of 15 Buddhist scriptures, sermons, and teachings. In order to shed light on how perception verb usage contributes to the understanding of complex Buddhist concepts, the goals are to reveal the semantic frames and metaphorical mappings connected to them. The research also examines how culture may have influenced the understanding of these verbs in this religious context. The research, through the integration of theoretical frameworks and meticulous qualitative techniques, provides valuable perspectives on the relationship between language, embodied cognition, and spiritual comprehension as expressed by perception verbs in Buddhist teachings.

The ADV speaking-construction in American English: A quantitative corpus-based investigation

Abstract In this article, the author applies the theory of frame semantics, a usage-based model of construction grammar, and quantitative corpus-based methodology to investigate the nature of the ADV speaking-construction in American English, an adverbial participle construction that has not been previously explored using quantitative corpus-based methods. To investigate this construction, the author extracted its occurrences from the Corpus of Contemporary American English (COCA), analysed its structural, semantic, distributional and discourse-functional properties, and identified adverbs that frequently appear in the construction. The investigation reveals that the construction tends to combine with speech-functional adverbs, which evoke different semantic frames. It commonly occurs in spoken and written registers and serves various functions in discourse. In particular, it is used frequently in spoken discourse and academic prose to comment on the manner of conveying a message and to express speakers’ stances and attitudes toward various topics.

Fluidic motion patterns in English and Modern Greek

Abstract This paper investigates conceptual and phraseological patterns from a cross-linguistic perspective. The focus of attention is on the fluidic motion uses of the highly polysemous verbs run and τρέχω ‘run’ in English and Modern Greek respectively. They are manner of motion verbs denoting the typically human, fast movement on ground and they are frequently cited in the literature on motion event encoding; yet, their extended use to denote motion of liquids is mentioned only in passing. The study thus provides a comprehensive description of that part of the semantic network of the two verbs that relates to fluidic motion (literally and figuratively). The contrastive approach taken combines cognitive semantics (Frame Semantics, Conceptual Metaphor and Metonymy Theory) with a phraseological view of language. Convergences and divergences are identified at a conceptual and phraseological level through a twofold corpus-based study involving comparable monolingual analysis and parallel corpus investigation.

Cognitive Modeling of the Sustainable Development of the Banking System under the Conditions of Innovative Transformations

The article considers theoretical approaches to modeling the sustainable development of the Russian banking system in the context of innovative transformations and the formation of digital ecosystems. The relevance of the study lies in the fact that in modern conditions, approaches are increasingly being used to ensure the sustainable development of the banking system based on cognitive modeling, the use of artifi cial intelligence. The scientifi c novelty lies in the fact that in the study, a deep learning model DL-model "Random Forest" was formed, which allows you to get a stable forecast for the net profi t of the banking system, in the conditions of innovative transformations. The practical signifi cance of the study is that the results obtained can be recommended for implementation in practice to provide support for managerial decision-making regarding the sustainable development of the banking system. The cognitive model was developed in the GraphViz environment using a semantic frame network in the form of graphs in the DOT programming language. An analysis of the dynamics of both macroeconomic indicators of the real sector of the economy and the parameters of the development of the banking sector of the Russian Federation was carried out. The criterion for the success of the predictive properties of the DL-model was the value of the average forecast error (MAE). The proposed DL model uses the best decision tree that has optimal hyperparameter settings, for example, the depth of the tree is six layers, the number of estimators (trees) in the ensemble is ten. In the experiment, the hyperparameters of the neural network did not change, the input parameters to various trees were randomly selected by the algorithm.

Text augmentation for semantic frame induction and parsing

Semantic frames are formal structures describing situations, actions or events, e.g., Commerce buy, Kidnapping, or Exchange. Each frame provides a set of frame elements or semantic roles corresponding to participants of the situation and lexical units (LUs)—words and phrases that can evoke this particular frame in texts. For example, for the frame Kidnapping, two key roles are Perpetrator and the Victim, and this frame can be evoked with lexical units abduct, kidnap, or snatcher. While formally sound, the scarce availability of semantic frame resources and their limited lexical coverage hinders the wider adoption of frame semantics across languages and domains. To tackle this problem, firstly, we propose a method that takes as input a few frame-annotated sentences and generates alternative lexical realizations of lexical units and semantic roles matching the original frame definition. Secondly, we show that the obtained synthetically generated semantic frame annotated examples help to improve the quality of frame-semantic parsing. To evaluate our proposed approach, we decompose our work into two parts. In the first part of text augmentation for LUs and roles, we experiment with various types of models such as distributional thesauri, non-contextualized word embeddings (word2vec, fastText, GloVe), and Transformer-based contextualized models, such as BERT or XLNet. We perform the intrinsic evaluation of these induced lexical substitutes using FrameNet gold annotations. Models based on Transformers show overall superior performance, however, they do not always outperform simpler models (based on static embeddings) unless information about the target word is suitably injected. However, we observe that non-contextualized models also show comparable performance on the task of LU expansion. We also show that combining substitutes of individual models can significantly improve the quality of final substitutes. Because intrinsic evaluation scores are highly dependent on the gold dataset and the frame preservation, and cannot be ensured by an automatic evaluation mechanism because of the incompleteness of gold datasets, we also carried out experiments with manual evaluation on sample datasets to further analyze the usefulness of our approach. The results show that the manual evaluation framework significantly outperforms automatic evaluation for lexical substitution. For extrinsic evaluation, the second part of this work assesses the utility of these lexical substitutes for the improvement of frame-semantic parsing. We took a small set of frame-annotated sentences and augmented them by replacing corresponding target words with their closest substitutes, obtained from best-performing models. Our extensive experiments on the original and augmented set of annotations with two semantic parsers show that our method is effective for improving the downstream parsing task by training set augmentation, as well as for quickly building FrameNet-like resources for new languages or subject domains.