scholarly journals Using semantic feature norms to investigate how the visual and verbal modes afford metaphor construction and expression

2016 ◽  
Vol 9 (3) ◽  
pp. 525-552 ◽  
Author(s):  
MARIANNA BOLOGNESI
Keyword(s):  
Semantic Similarity ◽  
Semantic Features ◽  
Linguistic Processing ◽  
Conceptual Content ◽  
Generation Task ◽  
Metaphor Comprehension ◽  
Conceptual Processing ◽  
Figures Of Speech ◽  
Visual Metaphors ◽  
Feature Norms

abstractIn this study, two modalities of expression (verbal and visual) are compared and contrasted, in relation to their ability and their limitations to construct and express metaphors. A representative set of visual metaphors and a representative set of linguistic metaphors are here compared, and the semantic similarity between metaphor terms is modeled within the two sets. Such similarity is operationalized in terms of semantic features produced by informants in a property generation task (e.g., McRae et al., 2005). Semantic features provide insights into conceptual content, and play a role in deep conceptual processing, as opposed to shallow linguistic processing. Thus, semantic features appear to be useful for modeling metaphor comprehension, assuming that metaphors are matters of thought rather than simple figures of speech (Lakoff & Johnson, 1980). The question tackled in this paper is whether semantic features can account for the similarity between metaphor terms of both visual and verbal metaphors. For this purpose, a database of semantic features was collected and then used to analyze fifty visual metaphors and fifty verbal metaphors. It was found that the number of semantic features shared between metaphor terms is predicted by the modality of expression of the metaphor: the terms compared in visual metaphors share semantic features, while the terms compared in verbal metaphors do not. This suggests that the two modalities of expression afford different ways to construct and express metaphors.

Bihemispheric Language: How the Two Hemispheres Collaborate in the Processing of Language

2004 ◽  
Author(s):  
Norman D. Cook
Keyword(s):  
Language Processing ◽  
Right Hemisphere ◽  
Central Question ◽  
Linguistic Processing ◽  
Figures Of Speech ◽  
Language Functions ◽  
Left And Right ◽  
Level Of Understanding ◽  
The Right

Speech production in most people is strongly lateralized to the left hemisphere (LH), but language understanding is generally a bilateral activity. At every level of linguistic processing that has been investigated experimentally, the right hemisphere (RH) has been found to make characteristic contributions, from the processing of the affective aspects of intonation, through the appreciation of word connotations, the decoding of the meaning of metaphors and figures of speech, to the understanding of the overall coherency of verbal humour, paragraphs and short stories. If both hemispheres are indeed engaged in linguistic decoding and both processes are required to achieve a normal level of understanding, a central question concerns how the separate language functions on the left and right are integrated. This chapter reviews relevant studies on the hemispheric contributions to language processing and the role of interhemispheric communications in cognition.

Experimentally Induced Single and Repeated Personal Name Confusions: The Impact of Phonological and Semantic Similarity

2019 ◽  
Vol 123 (3) ◽  
pp. 781-805
Author(s):  
Manuel Dupont
Keyword(s):  
Semantic Similarity ◽  
Learning Task ◽  
Phonological Similarity ◽  
Semantic Features ◽  
Experimental Paradigm ◽  
Semantic Code ◽  
Main Effect ◽  
First Results ◽  
Phonological Similarities ◽  
The Impact

Three experiments investigated a common but intriguing phenomenon, that is, repeated personal name confusion, a phenomenon at the border between language and memory. The purpose of those experiments was to evaluate the impact of the semantic and phonological similarities on name confusion and to compare repeated naming confusions (i.e., repeatedly confounding two names) with single confusions (i.e., confounding two names only once) in a same experimental paradigm. In all experiments, participants (64 middle-aged participants for each experiment) were asked to memorize the association between 16 names and 16 faces (face-name learning task). In Experiments 1 and 2, the two studied variables were the phonological similarity between the confused names and the semantic similarity between the two bearers of the confused names (using a visually derived semantic code in Experiment 1 and an identity-specific semantic code in Experiment 2). In Experiment 3, the impact of those two semantic similarities between the bearers of the confused names was taken into account, whereas the phonological similarity was not taken into account. First, results showed a main effect of the phonological and semantic similarity on name confusion (more confusions when the names were phonologically related or when the bearers of the names were semantically related). Second, we found that (1) the combination of the phonological and the semantic similarity and (2) the combination of the two semantic similarities led to an increase of name confusions. Third, in the three experiments, we found that the semantic and phonological similarities had a similar impact on repeated and single confusions. Finally, results showed that participants always made more single than repeated confusions, except in the case when the bearers of the confused names shared two semantic features.

Emotion Mining Using Semantic Similarity

2018 ◽  
Vol 9 (2) ◽  
pp. 1-22 ◽  
Author(s):  
Rafiya Jan ◽  
Afaq Alam Khan
Keyword(s):  
Semantic Similarity ◽  
Semantic Relatedness ◽  
Semantic Features ◽  
Emotion Detection ◽  
Emotion Classification ◽  
Textual Data ◽  
Different Types ◽  
Distributional Semantic Models ◽  
Affective Information

Social networks are considered as the most abundant sources of affective information for sentiment and emotion classification. Emotion classification is the challenging task of classifying emotions into different types. Emotions being universal, the automatic exploration of emotion is considered as a difficult task to perform. A lot of the research is being conducted in the field of automatic emotion detection in textual data streams. However, very little attention is paid towards capturing semantic features of the text. In this article, the authors present the technique of semantic relatedness for automatic classification of emotion in the text using distributional semantic models. This approach uses semantic similarity for measuring the coherence between the two emotionally related entities. Before classification, data is pre-processed to remove the irrelevant fields and inconsistencies and to improve the performance. The proposed approach achieved the accuracy of 71.795%, which is competitive considering as no training or annotation of data is done.

scholarly journals Suppression of semantic features in metaphor comprehension

2017 ◽  
Vol 4 (1) ◽  
pp. 1409323 ◽  
Author(s):  
Hassan Banaruee ◽  
Hooshang Khoshsima ◽  
Omid Khatin-Zadeh ◽  
Afsane Askari ◽  
Mireille Besson
Keyword(s):  
Semantic Features ◽  
Metaphor Comprehension

scholarly journals Recommendation Model Based on Semantic Features and a Knowledge Graph

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yudong Liu ◽  
Wen Chen
Keyword(s):  
Semantic Similarity ◽  
Information Science ◽  
Absolute Error ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Short Text ◽  
Model Based ◽  
Recommendation Algorithms ◽  
Tremendous Amount ◽  
Series Of Experiments

In the field of information science, how to help users quickly and accurately find the information they need from a tremendous amount of short texts has become an urgent problem. The recommendation model is an important way to find such information. However, existing recommendation models have some limitations in case of short text recommendation. To address these issues, this paper proposes a recommendation model based on semantic features and a knowledge graph. More specifically, we first select DBpedia as a knowledge graph to extend short text features of items and get the semantic features of the items based on the extended text. And then, we calculate the item vector and further obtain the semantic similarity degrees of the users. Finally, based on the semantic features of the items and the semantic similarity of the users, we apply the collaborative filtering technology to calculate prediction rating. A series of experiments are conducted, demonstrating the effectiveness of our model in the evaluation metrics of mean absolute error (MAE) and root mean square error (RMSE) compared with those of some recommendation algorithms. The optimal MAE for the model proposed in this paper is 0.6723, and RMSE is 0.8442. The promising results show that the recommendation effect of the model on the movie field is significantly better than those of these existing algorithms.

Property generation reflects word association and situated simulation

2011 ◽  
Vol 3 (1) ◽  
pp. 83-119 ◽  
Author(s):  
Ava Santos ◽  
Sergio E. Chaigneau ◽  
W. Kyle Simmons ◽  
Lawrence W. Barsalou
Keyword(s):  
Word Association ◽  
Sources Of Information ◽  
Conceptual Content ◽  
Generation Task ◽  
Verbal Responses ◽  
Complex Process ◽  
Alternative Sources ◽  
Feature Listing ◽  
Language Areas ◽  
Form System

AbstractThe property generation task (i.e. “feature listing”) is often assumed to measure concepts. Typically, researchers assume implicitly that the underlying representation of a concept consists of amodal propositions, and that verbal responses during property generation reveal their conceptual content. The experiments reported here suggest instead that verbal responses during property generation reflect two alternative sources of information: the linguistic form system and the situated simulation system. In two experiments, properties bearing a linguistic relation to the word for a concept were produced earlier than properties not bearing a linguistic relation, suggesting the early properties tend to originate in a word association process. Conversely, properties produced later tended to describe objects and situations, suggesting that late properties tend to originate from describing situated simulations. A companion neuroimaging experiment reported elsewhere confirms that early properties originate in language areas, whereas later properties originate in situated simulation areas. Together, these results, along with other results in the literature, indicate that property generation is a relatively complex process, drawing on at least two systems somewhat asynchronously.

scholarly journals An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Meijing Li ◽  
Tianjie Chen ◽  
Keun Ho Ryu ◽  
Cheng Hao Jin
Keyword(s):  
Semantic Similarity ◽  
Clustering Algorithms ◽  
Semantic Features ◽  
Text Data ◽  
Semantic Similarity Measure ◽  
Document Similarity ◽  
Hadoop Mapreduce ◽  
Semantic Mining ◽  
Semantic Similarity Measurement ◽  
Open Datasets

Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data. To solve this problem, we propose a parallelized semantic similarity measurement method based on Hadoop MapReduce for big text data. At first, we preprocess and extract the semantic features from documents. Then, we calculate the document semantic similarity based on ontology network structure under MapReduce framework. Finally, based on the generated semantic document similarity, document clusters are generated via clustering algorithms. To validate the effectiveness, we use two kinds of open datasets. The experimental results show that the traditional methods can hardly work for more than ten thousand biomedical documents. The proposed method keeps efficient and accurate for big dataset and is of high parallelism and scalability.

scholarly journals Metáforas e gráficos pictórico-esquemáticos de Nigel Holmes | Nigel Holme’s metaphors and pictorial-schematic charts

2019 ◽  
Vol 16 (3) ◽  
pp. 330-348
Author(s):  
Ricardo Oliveira da Cunha Lima
Keyword(s):  
Data Visualization ◽  
Cognitive Linguistics ◽  
Numerical Data ◽  
Information Design ◽  
Point Of View ◽  
Figures Of Speech ◽  
Visual Elements ◽  
Visual Metaphors ◽  
Graphic Language ◽  
Data Visualizations

Neste artigo, abordaremos metáforas visuais utilizadas na visualização de dados da infografia do célebre designer Nigel Holmes. Isto foi feito mediante o diálogo com a linguística cognitiva e a retórica visual, pela ótica da teoria de design da informação. Para tanto, nossa abordagem é embasada na teoria das metáforas cognitivas, marcadas pelos estudos de Lakoff e Johnson (1980), e a tradição de estudos de figuras de linguagem visual. Nesta análise utilizamos uma taxonomia de figuras de linguagem pictóricas utilizadas em gráficos estatísticos (LIMA, 2018). Ao analisarmos as metáforas pictóricas utilizadas por Holmes, observamos que este designer tem a tendência a sobrepor elementos pictóricos a elementos esquemáticos em seus gráficos estatísticos. Nós cunhamos esta mescla de modalidades gráficas de gráficos pictórico-esquemáticos. Este uso de elementos pictóricos, muitas vezes, humorísticos sobrepostos a dados numéricos precisos foi duramente combatida por teóricos do design da informação como Edward Tufte, na década de 1980. Estes elementos pictóricos foram chamados de chartjunk. Este termo tem servido como uma crítica à elementos visuais consideradas supérfluos em nome de uma abordagem mais neutra na infografia e visualização de dados. No entanto, procuramos entender a escolha do uso de metáforas visuais por Holmes como uma abordagem que não se limita a uma suposta neutralidade de linguagem gráfica.*****In this article, the focus is on visual metaphors used in Nigel Holmes’ data visualizations present in his infographics. This analysis was accomplished by approaching the theory of cognitive linguistics and visual rhetoric from the point of view of information design. Our study is based on the theory of cognitive metaphors, notably the work of Lakoff and Johnson (1980), and the study of figures of speech in visual language. In this analysis, we used a taxonomy of figures of speech for pictorial language in data visualization (LIMA, 2018). When analyzing the pictorial metaphors used by Holmes, we observe that this designer tends to overlap pictorial elements on schematic ones in his statistical charts. We coined this mix of graphic modalities: pictorial-schematic charts (gráficos pictórico-esquemáticos). This use of pictorial elements, often humorous, overlapping precise numerical data was harshly opposed by information design theorists such as Edward Tufte in the 1980s. These pictorial elements were called chartjunk. This term has served as a criticism of visual elements considered superfluous in the name of a more neutral approach to infographics and data visualization. However, we seek to understand Holmes' choice of using visual metaphors as an approach that is not limited to a supposedly neutral graphic language.

scholarly journals Linguistic and cognitive peculiarities of chinese cosmetics advertising

2020 ◽  
Vol 13 (22) ◽  
pp. 108-113
Author(s):  
Tetiana Vechorynska ◽  
◽  
Alina Snisarenko ◽  
Keyword(s):  
Influence Function ◽  
Cognitive Linguistics ◽  
World View ◽  
Semantic Content ◽  
Supplementary Information ◽  
Semantic Features ◽  
Expressive Vocabulary ◽  
Cognitive Approach ◽  
Figures Of Speech ◽  
Clear Idea

Based on the official data from Chinese Academy of Social Sciences in Beijing the present paper gives a comprehensive and clear idea of the current state of the cosmetics industry in China, thus, rising a question of advertising campaign efficiency focused on promotion of culture-oriented advertising texts. The paper explores linguistic and cognitive peculiarities of Chinese cosmetics advertising in the context of its functional specificity. By referring to such researchers as E. V. Medvedeva, U. V. Rozhdestvensky, and by tracing the etymological meaning of the word «advertising» the authors determine the influence function as the dominant one of the advertising text and conclude that influence function is performed through the following functional speech effects: clarity, emotional empathy effect, trust, dialogue and presence, i.e. all five featured in numerous verbal and non-verbal means of the language. The necessity of applying the linguistic and cognitive approach is thus determined. As part of this study, the linguistic and cognitive approach involves the analysis of lexical-semantic content, stylistic features, and cognitive, that is, national-specific, means of speech of advertising texts (slogans). The paper considers lexico-semantic features which include lexical transformations (loanwords and homonyms), wenyan vocabulary, expressive vocabulary, and the use of pronouns to address the potential consumer directly; stylistic features which include various figures of speech: parallelism and antithesis, rhyme and metaphors. The cognitive peculiarities refer to culture-specific national, philosophical and religious concepts, which emphasize the national identity and world view peculiarity of a Chinese customer. The paper presents the results of the investigation, and manifests the apparent relation between the above-mentioned features and the way a Chinese consumer percepts the advertising information. In other words, the paper determines the linguistic and cognitive peculiarities of the influence on a consumer, which is the dominant function of advertising. In conclusion, the authors outline the research prospects. It is supposed that the results of current investigation may contribute to the development of marketing strategies on promoting cosmetic products in China, as well as may be applied to designing specialized stylistics, lexicology, and cognitive linguistics courses. The data of this research may also provide some supplementary information for developing advertising and marketing courses.

Language statistics and individual differences in processing primary metaphors

2013 ◽  
Vol 24 (4) ◽  
pp. 667-687 ◽  
Author(s):  
Sterling Hutchinson ◽  
Max Louwerse
Keyword(s):  
Embodied Cognition ◽  
Cognitive Linguistics ◽  
Response Times ◽  
Linguistic Context ◽  
Metaphor Comprehension ◽  
Conceptual Processing ◽  
Temperature Experiment ◽  
Language Statistics ◽  
Metaphor Processing

AbstractResearch in cognitive linguistics has emphasized the role of embodiment in metaphor comprehension, with experimental research showing activation of perceptual simulations when processing metaphors. Recent research in conceptual processing has demonstrated that findings attributed to embodied cognition can be explained through language statistics. The current study investigates whether language statistics explain processing of primary metaphors and whether this effect is modified by the gender of the participant. Participants saw word pairs with valence (Experiment 1: good–bad), authority (Experiment 2: doctor–patient), temperature (Experiment 3: hot–cold), or gender (Experiment 4: male–female) connotations. The pairs were presented in either a vertical configuration (X above Y or Y above X) matching the primary metaphors (e.g., HAPPY IS UP, CONTROL IS UP) or a horizontal configuration (X left of Y or Y left of X) not matching the primary metaphors. Even though previous research has argued that primary metaphor processing can best be explained by an embodied cognition account, results demonstrate that statistical linguistic frequencies also explain the response times of the stimulus pairs both in vertical and horizontal configurations, because language has encoded embodied relations. In addition, the effect of the statistical linguistic frequencies was modified by participant gender, with female participants being more sensitive to statistical linguistic context than male participants.

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