Evolution of an Optimal Lexicon under Constraints from Embodiment

2003 ◽  
Vol 9 (4) ◽  
pp. 387-402 ◽  
Author(s):  
Willem Zuidema ◽  
Gert Westermann
Keyword(s):  
Cognitive Science ◽  
Language Processing ◽  
Language Evolution ◽  
Language Game ◽  
Self Organization ◽  
Language Games ◽  
Linguistic Patterns ◽  
Language And Cognition ◽  
Linguistic Structures

Research in language evolution is concerned with the question of how complex linguistic structures can emerge from the interactions between many communicating individuals. Thus it complements psycholinguistics, which investigates the processes involved in individual adult language processing, and child language development studies, which investigate how children learn a given (fixed) language. We focus on the framework of language games and argue that they offer a fresh and formal perspective on many current debates in cognitive science, including those on the synchronic-versus-diachronic perspective on language, the embodiment and situatedness of language and cognition, and the self-organization of linguistic patterns. We present a measure for the quality of a lexicon in a population, and derive four characteristics of the optimal lexicon: specificity, coherence, distinctiveness, and regularity. We present a model of lexical dynamics that shows the spontaneous emergence of these characteristics in a distributed population of individuals that incorporate embodiment constraints. Finally, we discuss how research in cognitive science could contribute to improving existing language game models.

Quality in qualitative management accounting research

2014 ◽  
Vol 11 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Hanne Nørreklit
Keyword(s):  
Cost Management ◽  
Management Accounting ◽  
Language Game ◽  
Language Games ◽  
Content Type ◽  
Four Dimensions ◽  
Accounting Research ◽  
Pragmatic Constructivism ◽  
Insight Into

Purpose – The purpose of this article is to demonstrate how the quality of Qualitative Research in Accounting & Management (QRAM) is manifested through the conceptualization of knowledge about functioning actions that are applicable for local management accounting practices. Design/methodology/approach – Drawing on language game theory and pragmatic constructivism, the paper analyzes the “practice doing” embedded in key language games of the case descriptions of three articles on intra-organizational buyer-supplier relations published in QRAM with the aim of revealing how they contribute to the development of a performativity in management accounting topos that integrates facts, possibilities, values and communication. Findings – The analysis documents that the three QRAM articles on inter-organizational cost management make a common contribution to the knowledge related to what to do to make functional actions within the practice of inter-organizational cost management. Together, the articles provide conceptual rigour with a complexity in content that can encompass the four dimensions of integration. Research limitations/implications – In providing a framework for analyzing practice relevance, the paper has implications for contemporary discussions on doing research that is relevant for practice. Originality/value – The paper provides novel insight into the analysis of quality in management accounting research. Additionally, it provides a framework for reflecting on the accumulation of practice-relevant knowledge and identifying areas requiring more research.

An Agent-Based Model for the Role of Short-Term Memory Enhancement in the Emergence of Grammatical Agreement

2018 ◽  
Vol 24 (2) ◽  
pp. 119-127
Author(s):  
Javier Vera
Keyword(s):  
Short Term Memory ◽  
Language Evolution ◽  
Language Game ◽  
Memory Capacity ◽  
Language Games ◽  
Short Term ◽  
Memory Enhancement ◽  
Term Memory ◽  
Grammatical Agreement ◽  
Multi Agent

What is the influence of short-term memory enhancement on the emergence of grammatical agreement systems in multi-agent language games? Agreement systems suppose that at least two words share some features with each other, such as gender, number, or case. Previous work, within the multi-agent language-game framework, has recently proposed models stressing the hypothesis that the emergence of a grammatical agreement system arises from the minimization of semantic ambiguity. On the other hand, neurobiological evidence argues for the hypothesis that language evolution has mainly related to an increasing of short-term memory capacity, which has allowed the online manipulation of words and meanings participating particularly in grammatical agreement systems. Here, the main aim is to propose a multi-agent language game for the emergence of a grammatical agreement system, under measurable long-range relations depending on the short-term memory capacity. Computer simulations, based on a parameter that measures the amount of short-term memory capacity, suggest that agreement marker systems arise in a population of agents equipped at least with a critical short-term memory capacity.

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

A cross-linguistic study of metacommunication in online hotel reviews

2020 ◽  
Vol 17 (4) ◽  
pp. 445-470
Author(s):  
Irene Cenni ◽  
Patrick Goethals ◽  
Camilla Vásquez
Keyword(s):  
Hospitality Industry ◽  
Service Providers ◽  
Mother Tongue ◽  
Service Failure ◽  
Specific Form ◽  
Service Encounter ◽  
Service Encounters ◽  
Correct Information ◽  
Linguistic Patterns

AbstractIn this study, we focus on a specific form of metacommunication found in an emerging digital genre: Hotel reviews posted on TripAdvisor. In particular, we investigate how tourists represent their service encounter interactions. The main goal of the present study is to identify what these digital metacommunicative practices reveal about communicative norms and expectations among groups of reviewers writing in three different languages. We analyzed a multilingual dataset of 1800 reviews written in English, Dutch, and Italian. The results reveal that reviewers commented upon a broad range of aspects when evaluating service encounters interactions, for instance, describing the quality of the interaction (e.g. polite, correct), or a lack of communication when a specific type of communication is expected (e.g. absence of greetings, or apologies after a service failure). Further, we found similar cross-linguistic patterns, such as appreciation for being able to communicate in one’s mother tongue during the hotel-guest encounter. At the same time, a few differences across languages emerged, such as the preference for precise and correct information within British reviews. Since service interactions are of fundamental importance for customer satisfaction, our findings contribute not only to the current research on metacommunication in digital contexts, but may also be significant for service providers in the hospitality industry.

scholarly journals Machine Learning for Dissimulating Reality

Proceedings ◽  
2021 ◽  
Vol 77 (1) ◽  
pp. 17
Author(s):  
Andrea Giussani
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Huge Amount ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Technological Advances ◽  
Textual Data ◽  
Musical Scores ◽  
Mathematical Formulas

In the last decade, advances in statistical modeling and computer science have boosted the production of machine-produced contents in different fields: from language to image generation, the quality of the generated outputs is remarkably high, sometimes better than those produced by a human being. Modern technological advances such as OpenAI’s GPT-2 (and recently GPT-3) permit automated systems to dramatically alter reality with synthetic outputs so that humans are not able to distinguish the real copy from its counteracts. An example is given by an article entirely written by GPT-2, but many other examples exist. In the field of computer vision, Nvidia’s Generative Adversarial Network, commonly known as StyleGAN (Karras et al. 2018), has become the de facto reference point for the production of a huge amount of fake human face portraits; additionally, recent algorithms were developed to create both musical scores and mathematical formulas. This presentation aims to stimulate participants on the state-of-the-art results in this field: we will cover both GANs and language modeling with recent applications. The novelty here is that we apply a transformer-based machine learning technique, namely RoBerta (Liu et al. 2019), to the detection of human-produced versus machine-produced text concerning fake news detection. RoBerta is a recent algorithm that is based on the well-known Bidirectional Encoder Representations from Transformers algorithm, known as BERT (Devlin et al. 2018); this is a bi-directional transformer used for natural language processing developed by Google and pre-trained over a huge amount of unlabeled textual data to learn embeddings. We will then use these representations as an input of our classifier to detect real vs. machine-produced text. The application is demonstrated in the presentation.

A ‘Galilean’ science of language

2014 ◽  
Vol 50 (3) ◽  
pp. 671-704 ◽  
Author(s):  
CHRISTINA BEHME
Keyword(s):  
Language Evolution ◽  
Relevant Literature ◽  
Child Psychology ◽  
Scientific Work ◽  
Poor Quality ◽  
The Poor ◽  
Recent Scholarship ◽  
Basic Understanding ◽  
Sixth Decade

The Science of Language, published in the sixth decade of Noam Chomsky's linguistic career, defends views that are visibly out of touch with recent research in formal linguistics, developmental child psychology, computational modeling of language acquisition, and language evolution. I argue that the poor quality of this volume is representative of the serious shortcomings of Chomsky's recent scholarship, especially of his criticism of and contribution to debates about language evolution. Chomsky creates the impression that he is quoting titbits of a massive body of scientific work he has conducted or is intimately familiar with. Yet his speculations reveal a lack of even basic understanding of biology, and an unwillingness to engage seriously with the relevant literature. At the same time, he ridicules the work of virtually all other theorists, without spelling out the views he disagrees with. A critical analysis of the ‘Galilean method’ demonstrates that Chomsky uses appeal to authority to insulate his own proposals against falsification by empirical counter-evidence. This form of discourse bears no serious relation to the way science proceeds.

Worchitect: An English Grammar (Parts of Speech) Card Game

2018 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Adi Idham Jailani ◽  
Nazarul Azali Razali ◽  
Ahmad Harith Syah Md Yusuf ◽  
Ariff Imran Anuar Yatim ◽  
Nor Atifah Mohamad
Keyword(s):  
Teaching And Learning ◽  
Language Game ◽  
Language Teachers ◽  
Learning Activity ◽  
Language Games ◽  
Card Game ◽  
Parts Of Speech ◽  
English Grammar ◽  
L2 Learners ◽  
Grammar Rules

Mastery of the English grammar is an intricate subject. Conventional teaching and learning of the English grammar have found to be an arduous task for teachers and a lacklustre one for students. The traditional pen and paper method often cause second language (L2) learners to become unmotivated in understanding this important element of the language. Thus, it is critical to provide L2 learners with the motivation to engage learning grammar in a more meaningful and purposive process. An ideal way to provide such learning experiences is through the use of language games that accommodate L2 learners’ desire to grasp grammar rules in an enjoyable way. To fill the gap for a purposive and meaningful grammar-based language game, Worchitect, a card-based game that focuses on (English) parts of speech is developed. The card game poses players/learners with questions that will foster their understanding of the rules of grammar for them to play the game and accumulate the highest scores possible. This game provides a constructive reinforcement to L2 users as it allows for the English parts of speech (and grammar) rules to be deductively attained. Furthermore, Worchitect is highly marketable as it is suitable for learners of various language proficiencies; for language teachers to be used as reinforcement or the actual learning activity; for parents who are looking to spend quality time with their children; and for any language enthusiast.

scholarly journals Association Between Patient Survival and Clinician Variability in Treatment Rates for Aortic Valve Stenosis

2021 ◽  
Author(s):  
J. Matthew Brennan ◽  
Angela Lowenstern ◽  
Paige Sheridan ◽  
Isabel J. Boero ◽  
Vinod H. Thourani ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Language Processing ◽  
Severe Aortic Stenosis ◽  
The United States ◽  
Risk Models ◽  
Transcatheter Aortic Valve ◽  
Competing Risk Models ◽  
The Impact ◽  
Potential Challenge

Background Patients with symptomatic severe aortic stenosis (ssAS) have a high mortality risk and compromised quality of life. Surgical/transcatheter aortic valve replacement (AVR) is a Class I recommendation, but it is unclear if this recommendation is uniformly applied. We determined the impact of managing cardiologists on the likelihood of ssAS treatment. Methods and Results Using natural language processing of Optum electronic health records, we identified 26 438 patients with newly diagnosed ssAS (2011–2016). Multilevel, multivariable Fine‐Gray competing risk models clustered by cardiologists were used to determine the impact of cardiologists on the likelihood of 1‐year AVR treatment. Within 1 year of diagnosis, 35.6% of patients with ssAS received an AVR; however, rates varied widely among managing cardiologists (0%, lowest quartile; 100%, highest quartile [median, 29.6%; 25th–75th percentiles, 13.3%–47.0%]). The odds of receiving AVR varied >2‐fold depending on the cardiologist (median odds ratio for AVR, 2.25; 95% CI, 2.14–2.36). Compared with patients with ssAS of cardiologists with the highest treatment rates, those treated by cardiologists with the lowest AVR rates experienced significantly higher 1‐year mortality (lowest quartile, adjusted hazard ratio, 1.22, 95% CI, 1.13–1.33). Conclusions Overall AVR rates for ssAS were low, highlighting a potential challenge for ssAS management in the United States. Cardiologist AVR use varied substantially; patients treated by cardiologists with lower AVR rates had higher mortality rates than those treated by cardiologists with higher AVR rates.

scholarly journals VLSP SHARED TASK: SENTIMENT ANALYSIS

2019 ◽  
Vol 34 (4) ◽  
pp. 295-310 ◽  
Author(s):  
Huyen T M Nguyen ◽  
Hung V Nguyen ◽  
Quyen T Ngo ◽  
Luong X Vu ◽  
Vu Mai Tran ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Objective Evaluation ◽  
Shared Task ◽  
Electronic Products ◽  
Performance Quality ◽  
Benchmark Datasets ◽  
Active Research ◽  
Evaluation Measurement

Sentiment analysis is a natural language processing (NLP) task of identifying orextracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encouragethe development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns.

scholarly journals Sentiment of App with Word Vectors

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2156-2159
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Data ◽  
Vector Representations ◽  
Text Sentiment Analysis

Vector representations for language have been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Sentiment Analysis. In particular, we target three sub-tasks namely sentiment words extraction, polarity of sentiment words detection, and text sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. Vector representations has been used to compute various vector-based features and conduct systematically experiments to demonstrate their effectiveness. Using simple vector based features can achieve better results for text sentiment analysis of APP.

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