New Computational Methods and the Study of the Romance Languages

2021 ◽  
Author(s):  
Basilio Calderone ◽  
Vito Pirrelli
Keyword(s):  
Computer Modeling ◽  
Language Processing ◽  
Theoretical Models ◽  
Romance Languages ◽  
Human Language ◽  
Theoretical Frameworks ◽  
Practical Applications ◽  
Huge Impact ◽  
Current Impact ◽  
Probabilistic Nature

Nowadays, computer models of human language are instrumental to millions of people, who use them every day with little if any awareness of their existence and role. Their exponential development has had a huge impact on daily life through practical applications like machine translation or automated dialogue systems. It has also deeply affected the way we think about language as an object of scientific inquiry. Computer modeling of Romance languages has helped scholars develop new theoretical frameworks and new ways of looking at traditional approaches. In particular, computer modeling of lexical phenomena has had a profound influence on some fundamental issues in human language processing, such as the purported dichotomy between rules and exceptions, or grammar and lexicon, the inherently probabilistic nature of speakers’ perception of analogy and word internal structure, and their ability to generalize to novel items from attested evidence. Although it is probably premature to anticipate and assess the prospects of these models, their current impact on language research can hardly be overestimated. In a few years, data-driven assessment of theoretical models is expected to play an irreplaceable role in pacing progress in all branches of language sciences, from typological and pragmatic approaches to cognitive and formal ones.

Semantic Priming in Monolingual Russian and Bilingual Russian (L1)-English (L2) Speakers in a Single Word Naming Task

2022 ◽  
pp. 270-289
Author(s):  
Evgenia Volkovyskaya ◽  
Ilhan Raman ◽  
Bahman Baluch
Keyword(s):  
Semantic Priming ◽  
Language Processing ◽  
Semantic Processing ◽  
Theoretical Models ◽  
Naming Task ◽  
Theoretical Frameworks ◽  
Russian Speakers ◽  
L2 Speaking ◽  
Semantic Priming Effect ◽  
L1 English

Identifying and exploring factors that influence bilingual language processing has been the topic of much psycholinguistic research. Semantic priming is typically used to examine semantic processing and refers to the phenomenon in which semantically related items (doctor-nurse) are processed faster and more accurately than semantically unrelated items (doctor-butter). The aim of the chapter is to address two key questions: 1) how the two languages of a bilingual are organised or stored and 2) how the two languages are processed. A review of the literature shows that there are currently no theoretical frameworks that explain Russian monolingual or Russian (L1)-English (L2) bilingual storage or processing. Monolingual Russian speakers and bilingual Russian (L1)-English (L2) speaking university students were asked to name target words under related or unrelated conditions. The results show that the magnitude of the semantic priming effect was determined by L2 proficiency. The implications for these findings is discussed within the current bilingual theoretical models.

scholarly journals Semantic Priming in Monolingual Russian and Bilingual Russian (L1)-English (L2) Speakers in a Single Word Naming Task

2018 ◽  
pp. 87-111
Author(s):  
Evgenia Volkovyskaya ◽  
Ilhan Raman ◽  
Bahman Baluch
Keyword(s):  
Semantic Priming ◽  
Language Processing ◽  
Semantic Processing ◽  
Theoretical Models ◽  
Naming Task ◽  
Theoretical Frameworks ◽  
Russian Speakers ◽  
L2 Speaking ◽  
Semantic Priming Effect ◽  
L1 English

Identifying and exploring factors that influence bilingual language processing has been the topic of much psycholinguistic research. Semantic priming is typically used to examine semantic processing and refers to the phenomenon in which semantically related items (doctor-nurse) are processed faster and more accurately than semantically unrelated items (doctor-butter). The aim of the chapter is to address two key questions: 1) how the two languages of a bilingual are organised or stored and 2) how the two languages are processed. A review of the literature shows that there are currently no theoretical frameworks that explain Russian monolingual or Russian (L1)-English (L2) bilingual storage or processing. Monolingual Russian speakers and bilingual Russian (L1)-English (L2) speaking university students were asked to name target words under related or unrelated conditions. The results show that the magnitude of the semantic priming effect was determined by L2 proficiency. The implications for these findings is discussed within the current bilingual theoretical models.

In Search of a Human Language Processing System

1985 ◽  
Vol 30 (7) ◽  
pp. 529-531
Author(s):  
Patrick Carroll
Keyword(s):  
Language Processing ◽  
Processing System ◽  
Human Language

Minimizing the cost of products manufactured by the assembly line

2020 ◽  
pp. 483-487
Author(s):  
N.I. Aristova
Keyword(s):  
Computer Modeling ◽  
Assembly Line ◽  
Scientific Research ◽  
The Self ◽  
Manufactured Products ◽  
The Cost ◽  
Assembly Operations ◽  
Automation Tools ◽  
Probabilistic Nature ◽  
Product Assembly

A significant criterion for the functioning of an assembly line is to minimize the cost of manufactured products, for the achievement of which approaches are currently used that apply computer modeling and the hierarchical principle of product assembly, the approach, as well as taking into account the probabilistic nature of the assembly operations. An overview of scientific research aimed at solving these problems is given. An approach has been proposed that makes it possible to assess the efficiency of production in the self-reproduction of automation tools by the criterion of minimizing the cost of manufactured products.

scholarly journals Sentence Generation for Indian Sign Language Using NLP

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 196-210
Author(s):  
Dr.P. Golda Jeyasheeli ◽  
N. Indumathi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Sign Language ◽  
Language Processing ◽  
Normal Person ◽  
Human Language ◽  
Android Application ◽  
Sentence Generation ◽  
Normal People ◽  
Indian Sign Language

Nowadays the interaction among deaf and mute people and normal people is difficult, because normal people scuffle to understand the sense of the gestures. The deaf and dumb people find problem in sentence formation and grammatical correction. To alleviate the issues faced by these people, an automatic sign language sentence generation approach is propounded. In this project, Natural Language Processing (NLP) based methods are used. NLP is a powerful tool for translation in the human language and also responsible for the formation of meaningful sentences from sign language symbols which is also understood by the normal person. In this system, both conventional NLP methods and Deep learning NLP methods are used for sentence generation. The efficiency of both the methods are compared. The generated sentence is displayed in the android application as an output. This system aims to connect the gap in the interaction among the deaf and dumb people and the normal people.

At-Risk Youth: Music-Making as a Means to Promote Positive Relationships

2012 ◽  
pp. 184-202 ◽  
Author(s):  
Mary L. Cohen ◽  
Laya H. Silber ◽  
Andrea Sangiorgio ◽  
Valentina Iadeluca
Keyword(s):  
At Risk ◽  
Music Education ◽  
Deviant Behavior ◽  
At Risk Youth ◽  
Theoretical Frameworks ◽  
Practical Applications ◽  
Music Making ◽  
Reflective Questions ◽  
Youth Music ◽  
Key Terms

This article examines music programs for at-risk youth and their implications for music education and community music practices. It defines key terms, examines theoretical frameworks related to teaching at-risk youth, and describes practical applications of these frameworks. It discusses philosophies for addressing deviant behavior and controlling modes imposed from the outside to systems of cooperation. It is argued that cooperative systems are effective in facilitating music-making by for at-risk youth. The article concludes with implications for music education, suggestions for advocacy considerations, reflective questions, and a list of additional sources.

Better Performance with Transformer: CPPFormer in precise prediction of cell-Penetrating Peptides

2021 ◽  
Vol 28 ◽  
Author(s):  
Yuyang Xue ◽  
Xiucai Ye ◽  
Lesong Wei ◽  
Xin Zhang ◽  
Tetsuya Sakurai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Cell Penetrating Peptides ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Practical Applications ◽  
Deep Model ◽  
Cell Penetrating ◽  
Precise Prediction ◽  
Transformer Model

: With its superior performance, the Transformer model, which is based on the 'Encoder-Decoder' paradigm, has become the mainstream in natural language processing. On the other hand, bioinformatics has embraced machine learning and made great progress in drug design and protein property prediction. Cell-penetrating peptides (CPPs) are one kind of permeable protein that is convenient as a kind of 'postman' in drug penetration tasks. However, a small number of CPPs have been discovered by research, let alone practical applications in drug permeability. Therefore, correctly identifying the CPPs has opened up a new way to take macromolecules into cells without other potentially harmful materials in the drug. Most of the previous work only uses trivial machine learning techniques and hand-crafted features to construct a simple classifier. In CPPFormer, we learn from the idea of implementing the attention structure of Transformer, rebuilding the network based on the characteristics of CPPs according to its short length, and using an automatic feature extractor with a few manual engineered features to co-direct the predicted results. Compared to all previous methods and other classic text classification models, the empirical result has shown that our proposed deep model-based method has achieved the best performance of 92.16% accuracy in the CPP924 dataset and has passed various index tests.

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