scholarly journals Contextual Information Helps Understand Messages Written with Textisms

2021 ◽  
Vol 11 (11) ◽  
pp. 4853
Author(s):  
Baptiste Jacquet ◽  
Caline Jaraud ◽  
Frank Jamet ◽  
Sabine Guéraud ◽  
Jean Baratgin
Keyword(s):  
Prior Knowledge ◽  
Contextual Information ◽  
Random Order ◽  
Relevance Theory ◽  
Turing Test ◽  
Written Language ◽  
Writing Style ◽  
Online Conversation ◽  
Cognitive Cost ◽  
Do So

The present study investigated the influence of the use of textisms, a form of written language used in phone-mediated conversations, on the cognitive cost of French participants in an online conversation. Basing our thinking on the relevance theory of Sperber and Wilson, we tried to assess whether knowing the context and topic of a conversation can produce a significant decrease in the cognitive cost required to read messages written in textism by giving additional clues to help infer the meaning of these messages. In order to do so, participants played the judges in a Turing test between a normal conversation (written with the traditional writing style) and a conversation in which the experimenter was conversing with textisms, in a random order. The results indicated that participants answered messages written in textism faster when they were in the second conversation. We concluded that prior knowledge about the conversation can help interpret the messages written in textisms by decreasing the cognitive cost required to infer their meaning.

scholarly journals Pragmatic Aspect of Translation: The Interpretation-Based Inference and Its Implications for Translation

2017 ◽  
Author(s):  
AWEJ for Translation & Literary Studies ◽  
Rafat Y. Alwazna
Keyword(s):  
Contextual Information ◽  
Relevance Theory ◽  
Semantic Content ◽  
Source Text ◽  
Current Context ◽  
Contextual Detail ◽  
Pragmatic Aspect ◽  
Communicative Act ◽  
The Right ◽  
Do So

The elements of encoding, transferring and decoding are crucial in all processes of communication, however, drawing the appropriate inference from the current context is equally important in communication according to relevance theory (Gutt, 1998, p. 41). Semantic content is not always sufficient to fully comprehend the exact meaning of a particular utterance as the meaning of that utterance may hinge upon the contextual detail with which it is inferentially associated. The success of the process of communication relies on whether or not the recipient makes use of the context intended by the speaker. Failure to do so would give rise to miscommunication (Gutt, 1998, p. 42). Translation, as a communicative act, involves interpretation made by the translator, which takes the context of the target text (TT) reader and his/her knowledge into consideration. The present paper argues that even though the translator, according to relevance theory, is required to reproduce a TT that can stand as a faithful rendering of the source text (ST), the translator, however, needs to make his/her translated text relevant to the target reader. This, in many instances, may demand following certain procedures of explications in the TT to equip the target reader with the relevant contextual information needed to draw the appropriate inferences from the utterance concerned, and therefore make the right interpretation. Such exegesis needs to be added to the target text as what is inferable for the ST user may not be inferable for the TT receiver owing to cognitive and cultural differences.

scholarly journals Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3848
Author(s):  
Wei Cui ◽  
Meng Yao ◽  
Yuanjie Hao ◽  
Ziwei Wang ◽  
Xin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Contextual Information ◽  
Information Aggregation ◽  
Semantic Segmentation ◽  
Spatial Correlations ◽  
Convolutional Network ◽  
Object Based ◽  
Graph Neural Networks ◽  
Salt And Pepper

Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

Quel corpus pour l’étude du dialecte niçois ?

2021 ◽  
Vol 137 (2) ◽  
pp. 344-361
Author(s):  
Philippe Del Giudice
Keyword(s):  
Middle Ages ◽  
Written Language ◽  
Linguistic Features ◽  
Descriptive Grammar ◽  
Written Production ◽  
Starting Point ◽  
Social Use ◽  
Over Time ◽  
Made In ◽  
Do So

Abstract A new project has just been launched to write a synchronic, descriptive grammar of Niçois, the Occitan dialect of Nice. In this article, I define the corpus of the research. To do so, I first review written production from the Middle Ages to the present. I then analyze the linguistic features of Niçois over time, in order to determine the precise starting point of the current language state. But because of reinforced normativism and the decreasing social use of Niçois among the educated population, written language after WWII became artificial and does not really correspond to recordings made in the field. The corpus will thus be composed of writings from the 1820’s to WWII and recordings from the last few decades.

Prescribing French: A corpus-linguistic approach to official terminology in French newspapers

2020 ◽  
pp. 1-24
Author(s):  
Gyula Zsombok
Keyword(s):  
Computer Science ◽  
Negative Influence ◽  
Written Language ◽  
Entertainment Industry ◽  
Dynamic Approach ◽  
French Academy ◽  
Corpus Linguistic ◽  
Linguistic Approach ◽  
Lexical Fields ◽  
Do So

ABSTRACT In France, English is often perceived as a negative influence on the language in the eyes of purist institutions like the French Academy. Terminological commissions have been established to replace foreign expressions with French terminology that is regularly published in the Journal officiel de la République française. Although the Toubon Law of 1994 prescribes the use of this terminology in government publications, speakers are merely encouraged to do so. This article investigates the variation between English lexical borrowings and their prescribed equivalents in a large newspaper corpus containing articles from 2000 to 2017 in order to see whether formal written language complies with the purist recommendations. Time is treated with a new dynamic approach: the probability of using a prescribed term is estimated three years before and three years after official prescription. Fifty-four target terms are selected from the lexical fields of computer science, entertainment industry and telecommunication, including emblematic prescribed words such as courriel and mot-dièse. The analysis reveals that prescription is only effective when it follows already attested use. Furthermore, conservative newspapers show higher proportions of recommended terminology, especially as compared to newspapers specializing in technology.

scholarly journals The MODES toolbox: Measurements of Open-ended Dynamics in Evolving Systems

2018 ◽  
Author(s):  
Emily L Dolson ◽  
Anya E Vostinar ◽  
Michael J Wiser ◽  
Charles A Ofria
Keyword(s):  
Evolutionary Computation ◽  
Prior Knowledge ◽  
Artificial Life ◽  
Digital Evolution ◽  
Evolutionary Systems ◽  
Major Transitions ◽  
Evolving Systems ◽  
Experimental Systems ◽  
Nk Landscapes ◽  
Do So

Building more open-ended evolutionary systems can simultaneously advance our understanding of biology, artificial life, and evolutionary computation. In order to do so, however, we need a way to determine when we are moving closer to this goal. We propose a set of metrics that allow us to measure a system's ability to produce commonly-agreed-upon hallmarks of open-ended evolution: change potential, novelty potential, complexity potential, and ecological potential. Our goal is to make these metrics easy to incorporate into a system, and comparable across systems so that we can make coherent progress as a field. To this end, we provide detailed algorithms (including C++ implementations) for these metrics that should be easy to incorporate into existing artificial life systems. Furthermore, we expect this toolbox to continue to grow as researchers implement these metrics in new languages and as the community reaches consensus about additional hallmarks of open-ended evolution. For example, we would welcome a measurement of a system's potential to produce major transitions in individuality. To confirm that our metrics accurately measure the hallmarks we are interested in, we test them on two very different experimental systems: NK Landscapes and the Avida Digital Evolution Platform. We find that our observed results are consistent with our prior knowledge about these systems, suggesting that our proposed metrics are effective and should generalize to other systems.

Translation of strategic ambiguity: A relevance-theoretic analysis

2018 ◽  
Vol 54 (1) ◽  
pp. 1-35 ◽  
Author(s):  
Rasheed S. Al-Jarrah ◽  
Ahmad M. Abu-Dalu ◽  
Hisham Obiedat
Keyword(s):  
Conceptual Framework ◽  
Ambiguity Resolution ◽  
Contextual Information ◽  
Relevance Theory ◽  
Original Text ◽  
Theoretic Analysis ◽  
Strategic Ambiguity ◽  
The One ◽  
Point Of Departure

AbstractThe purpose of our current research is to see how Relevance Theory can handle one specific translation problem, namely strategic ambiguous structures. Concisely, we aim to provide a conceptual framework as to how the translator should cope with a pervasive ambiguity problem at the discoursal level. The point of departure from probably all previous models of analysis is that a relevance-theoretic analysis would, we believe, require that a “good” translation benotthe one that representsan interpretationof the text, but the one which leaves the door open for all interpretations which the original text provides evidence for. Hence,the role of translator is not to ‘interpret’ but to ‘translate’. If this is true, ambiguity resolution should not be a viable alternative. In other words, what the translator should do is empower the audience with all it takes to let them work out all the explicatures (linguistically inferred meanings) and entertain themselves with the implicatures (contextually inferred meanings) of the original. Direct Translation, along the lines laid down by Gutt (1991/2000), is the method of translation which can, we believe, bring about the desired results because “it tries to provide readers with contextual information that enables them to draw their own inferences” (Smith 2000: 92).

Adapting Relevance Theory to Accommodate Visual Communication

2020 ◽  
pp. 63-98
Author(s):  
Charles Forceville
Keyword(s):  
Visual Communication ◽  
Logical Form ◽  
Relevance Theory ◽  
Written Language ◽  
Relevance Principle ◽  
Static Visuals

On the basis of relevance theory’s claim that the relevance principle underlies all forms of communication, Chapter 3 examines how the RT model can be applied to communication involving visuals, and what adaptations are called for to achieve this goal. After reflecting on what constitutes visual communication, and showing how static visuals often combine with written language to create multimodal meaning, all of the RT concepts discussed in Chapter 2 are reconsidered with reference to their pertinence to analyzing visuals. This reconsideration will not only benefit visual and multimodal theories but will also provide new angles on classic RT. Whereas many RT concepts function without any problem when applied to visuals, there are others that cannot straightforwardly be “translated” to the visual realm and therefore need adaptation. The problematic issues mainly result from the fact that visuals typically have a structure and depicted entities, but no grammar and vocabulary. This leads to the questions of whether visuals can nonetheless be “coded”—which in turn has consequences for their possible underlying “logical form”—and whether information in visuals is necessarily always to be inferred or is sometimes actually decoded. Several examples are discussed to clarify these issues. In the final sections, there is a brief discussions of the relation between RT and Blending Theory, and of RT’s problematic take on metaphor.

scholarly journals Fertility as a constraint on group size in African great Apes

2019 ◽  
Author(s):  
R I M Dunbar
Keyword(s):  
Group Size ◽  
Environmental Effects ◽  
Social Groups ◽  
Great Apes ◽  
Adult Females ◽  
African Great Apes ◽  
Cognitive Cost ◽  
Size And Structure ◽  
Do So ◽  
The Way

Abstract Gorillas and chimpanzees live in social groups of very different size and structure. Here I test the hypothesis that this difference might reflect the way fertility maps onto group demography as it does in other Catarrhines. For both genera, birth rates and the number of surviving offspring per female are quadratic (or ∩-shaped) functions of the number of adult females in the group, and this is independent of environmental effects. The rate at which fertility declines ultimately imposes a constraint on the size of social groups that can be maintained in both taxa. The differences in group size between the two genera seem to reflect a contrast in the way females buffer themselves against this cost. Gorillas do this by using males as bodyguards, whereas chimpanzees exploit fission–fusion sociality to do so. The latter allows chimpanzees to live in much larger groups without paying a fertility cost (albeit at a cognitive cost).

scholarly journals How do the Questions Asked Affect Suspects’ Perceptions of the Interviewer’s Prior Knowledge?

2019 ◽  
Author(s):  
Meghana Srivatsav ◽  
Timothy John Luke ◽  
Pär Anders Granhag ◽  
Aldert Vrij
Keyword(s):  
Prior Knowledge ◽  
Prior Information ◽  
Relevance Theory ◽  
Related Topic ◽  
Underlying Mechanisms ◽  
Interview Transcript ◽  
Different Levels

The aim of this study was to understand if guilty suspects’ perceptions regarding the prior information or evidence held by the interviewer against the suspect could be influenced through the content of the investigative questions. To test this idea, we explored three question-phrasing factors that we labeled as Topic Discussion (if a specific crime-related topic was discussed or not), Specificity (different levels of crime-related details included in the questions) and Stressor (emphasis on the importance of the specific crime-related detail in the questions). The three factors were chosen based on relevance theory, a psycholinguistic theory that explores how people draw inferences from the communicated content. Participants (N= 370) assumed the role of the suspect and read a crime narrative and an interview transcript based on the suspect’s activities. After reading the narrative and the transcripts, participants responded to scales that measured their perception of interviewer’s prior knowledge (PIK) regarding the suspects’ role in the crime, based on the questions posed by the interviewer in the transcripts. Of the three factors tested, we found that questioning about a specific crime-related topic (Topic Discussion) increased their PIK. This study is the first to explore the underlying mechanisms of how suspects draw inferences regarding the interviewer’s prior knowledge through the content of the investigative questions adopting concepts of psycholinguistic theory.

scholarly journals Linking intrinsic and apparent relationships between phytoplankton and environmental forcings using machine learning ‐ What are the challenges?

2020 ◽  
Author(s):  
Christopher Holder ◽  
Anand Gnanadesikan
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Complex Systems ◽  
Spatial Scale ◽  
Prior Knowledge ◽  
Systematic Study ◽  
The Other ◽  
Future Research ◽  
Laboratory Setting ◽  
Do So

Abstract. Controls on phytoplankton growth are typically determined in two ways: by varying one driver of growth at a time such as nutrient or light in a controlled laboratory setting (intrinsic relationships) or by observing the emergence of relationships in the environment (apparent relationships). However, challenges remain when trying to take the intrinsic relationships found in a lab and scaling them up to the size of ecosystems (i.e., linking intrinsic relationships in the lab to apparent relationships in large ecosystems). We investigated whether machine learning (ML) techniques could help bridge this gap. ML methods have many benefits, including the ability to accurately predict outcomes in complex systems without prior knowledge. Although previous studies have found that ML can find apparent relationships, there has yet to be a systematic study that has examined when and why these apparent relationships will diverge from the underlying intrinsic relationships. To investigate this question, we created three scenarios: one where the intrinsic and apparent relationships operate on the same time and spatial scale, another model where the intrinsic and apparent relationships have different timescales but the same spatial scale, and finally one in which we apply ML to actual ESM output. Our results demonstrated that when intrinsic and apparent relationships are closely related and operate on the same spatial and temporal timescale, ML is able to extract the intrinsic relationships when only provided information about the apparent relationships. However, when the intrinsic and apparent relationships operated on different timescales (as little separation as hourly to daily), the ML methods underestimated the biomass in the intrinsic relationships. This was largely attributable to the decline in the variation of the measurements; the hourly time series had higher variability than the daily, weekly, and monthly-averaged time series. Although the limitations found by ML were overestimated, they were able to produce more realistic shapes of the actual relationships compared to MLR. Future research may use this type of information to investigate which nutrients affect the biomass most when values of the other nutrients change. From our study, it appears that ML can extract useful information from ESM output and could likely do so for observational datasets as well.

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