scholarly journals Cognitive rehabilitation of naming deficits following viral meningo-encephalitis

2002 ◽  
Vol 60 (1) ◽  
pp. 21-27 ◽  
Author(s):  
Eliane C. Miotto
Keyword(s):  
Everyday Life ◽  
Language Processing ◽  
Visual Imagery ◽  
Cognitive Rehabilitation ◽  
Individual Characteristics ◽  
Word Retrieval ◽  
Semantic Association ◽  
Life Context ◽  
Naming Deficits

OBJECTIVE: This case study describes the neuropsychological assessment and cognitive rehabilitation of a patient who developed word retrieval deficits for objects and people's names, following an episode of viral meningo-encephalitits. It shows the implementation and outcome of two techniques adapted to the patient's individual characteristics and context providing a more ecologically valid approach. METHODS: In the first technique, "verbal semantic association", the patient was required to describe what she knew about an object as a strategy to help her retrieve its name. In the second one, "face-name association" she was taught to apply a visual-imagery technique in order to retrieve relevant people's names. RESULTS: Following the implementation of these procedures there was a decrease in the number of episodes of failure to retrieve objects and people's names in her everyday life context. CONCLUSION: The improvement found in the patient's ability to retrieve words is discussed in terms of the utility of cognitive rehabilitation programmes and cognitive models of language processing

scholarly journals Learning from, through and about Differences: A Multiple Case Study on Schools as Practice Grounds for Citizenship

2021 ◽  
Vol 10 (6) ◽  
pp. 200
Author(s):  
Willemijn F. Rinnooy Kan ◽  
Virginie März ◽  
Monique Volman ◽  
Anne Bert Dijkstra
Keyword(s):  
Cultural Diversity ◽  
Citizenship Education ◽  
Multiple Case Study ◽  
Individual Characteristics ◽  
Societal Implications ◽  
Formal Curriculum ◽  
Teacher Student ◽  
Multiple Case ◽  
Support Students

Learning to relate to others that differ from you is one of the central aims of citizenship education. Schools can be understood as practice grounds for citizenship, where students’ citizenship is not only influenced by the formal curriculum, but also by their experiences in the context of teacher–student and student–student relations. In this article we therefore investigate how the practice of dealing with difference is enacted in schools. Data were collected through an exploratory multiple case study in four secondary schools, combining interviews and focus groups. Despite the differences between the schools in terms of population and location, in all schools the reflection on the enactment of ‘dealing with differences’ was limited in scope and depth. ‘Being different’ was understood primarily in terms of individual characteristics. Furthermore, in all schools there was limited reflection on being different in relation to teachers and the broader community. Finally, relevant differences for citizenship were confined to the category of ‘ethnic and cultural diversity’. This article calls for preparing teachers to consider a broader array of differences to practice dealing with differences with their students and to support students in reflecting on the societal implications of being different from each other.

scholarly journals Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Jacqueline Peng ◽  
Mengge Zhao ◽  
James Havrilla ◽  
Cong Liu ◽  
Chunhua Weng ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Clinical Manifestations ◽  
Predictive Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Research Articles

Abstract Background Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.

scholarly journals Citizen Participation and Machine Learning for a Better Democracy

2021 ◽  
Author(s):  
Robert Procter ◽  
Miguel Arana-Catania ◽  
Felix-Anselm van Lier ◽  
Nataliya Tkachenko ◽  
Yulan He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Citizen Participation ◽  
Language Processing ◽  
Information Overload ◽  
City Council ◽  
Decision Making Processes ◽  
Development Goals ◽  
Democratic Systems ◽  
The City

The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations. In this article, we report on the progress of a project that aims to address barriers, one of which is information overload, to achieving effective direct citizen participation in democratic decision-making processes. The main objectives are to explore if the application of Natural Language Processing (NLP) and machine learning can improve citizens? experience of digital citizen participation platforms. Taking as a case study the ?Decide Madrid? Consul platform, which enables citizens to post proposals for policies they would like to see adopted by the city council, we used NLP and machine learning to provide new ways to (a) suggest to citizens proposals they might wish to support; (b) group citizens by interests so that they can more easily interact with each other; (c) summarise comments posted in response to proposals; (d) assist citizens in aggregating and developing proposals. Evaluation of the results confirms that NLP and machine learning have a role to play in addressing some of the barriers users of platforms such as Consul currently experience.

scholarly journals The Sentiments Analysis of Donald Trump and Jokowi’s Twitters on Covid-19 Policy Dissemination

Webology ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 389-405
Author(s):  
Rahmad Agus Dwianto ◽  
Achmad Nurmandi ◽  
Salahudin Salahudin
Keyword(s):  
Data Collection ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
The Us ◽  
Twitter Account ◽  
Policy Dissemination ◽  
Collection Period ◽  
Source Materials

As Covid-19 spreads to other nations and governments attempt to minimize its effect by introducing countermeasures, individuals have often used social media outlets to share their opinions on the measures themselves, the leaders implementing them, and the ways in which their lives are shifting. Sentiment analysis refers to the application in source materials of natural language processing, computational linguistics, and text analytics to identify and classify subjective opinions. The reason why this research uses a sentiment case study towards Trump and Jokowi's policies is because Jokowi and Trump have similarities in handling Covid-19. Indonesia and the US are still low in the discipline in implementing health protocols. The data collection period was chosen on September 21 - October 21 2020 because during that period, the top 5 trending on Twitter included # covid19, #jokowi, #miglobal, #trump, and #donaldtrump. So, this period is most appropriate for taking data and discussing the handling of Covid-19 by Jokowi and Trump. The result shows both Jokowi and Trump have higher negative sentiments than positive sentiments during the period. Trump had issued a controversial statement regarding the handling of Covid-19. This research is limited to the sentiment generated by the policies conveyed by the US and Indonesian Governments via @jokowi and @realDonaldTrump Twitter Account. The dataset presented in this research is being collected and analyzed using the Brand24, a software-automated sentiment analysis. Further research can increase the scope of the data and increase the timeframe for data collection and develop tools for analyzing sentiment.

scholarly journals Ekstraksi Informasi Halaman Web Menggunakan Pendekatan Bootstrapping pada Ontology-Based Information Extraction

2015 ◽  
Vol 9 (2) ◽  
pp. 111 ◽  
Author(s):  
Erma Susanti ◽  
Khabib Mustofa
Keyword(s):  
Information Extraction ◽  
Language Processing ◽  
Semantic Content ◽  
Extraction Process ◽  
Web Pages ◽  
Structured Information ◽  
Improved Performance ◽  
Types Of Information ◽  
Unstructured Information

AbstrakEkstraksi  informasi  merupakan suatu bidang ilmu untuk pengolahan bahasa alami, dengan cara mengubah teks tidak terstruktur menjadi informasi dalam bentuk terstruktur. Berbagai jenis informasi di Internet ditransmisikan secara tidak terstruktur melalui website, menyebabkan munculnya kebutuhan akan suatu teknologi untuk menganalisa teks dan menemukan pengetahuan yang relevan dalam bentuk informasi terstruktur. Contoh informasi tidak terstruktur adalah informasi utama yang ada pada konten halaman web. Bermacam pendekatan untuk ekstraksi informasi telah dikembangkan oleh berbagai peneliti, baik menggunakan metode manual atau otomatis, namun masih perlu ditingkatkan kinerjanya terkait akurasi dan kecepatan ekstraksi. Pada penelitian ini diusulkan suatu penerapan pendekatan ekstraksi informasi dengan mengkombinasikan pendekatan bootstrapping dengan Ontology-based Information Extraction (OBIE). Pendekatan bootstrapping dengan menggunakan sedikit contoh data berlabel, digunakan untuk memimalkan keterlibatan manusia dalam proses ekstraksi informasi, sedangkan penggunakan panduan ontologi untuk mengekstraksi classes (kelas), properties dan instance digunakan untuk menyediakan konten semantik untuk web semantik. Pengkombinasian kedua pendekatan tersebut diharapkan dapat meningkatan kecepatan proses ekstraksi dan akurasi hasil ekstraksi. Studi kasus untuk penerapan sistem ekstraksi informasi menggunakan dataset “LonelyPlanet”. Kata kunci—Ekstraksi informasi, ontologi, bootstrapping, Ontology-Based Information Extraction, OBIE, kinerja Abstract Information extraction is a field study of natural language processing by converting unstructured text into structured information. Several types of information on the Internet is transmitted through unstructured information via websites, led to emergence of the need a technology to analyze text and found relevant knowledge into structured information. For example of unstructured information is existing main information on the content of web pages. Various approaches  for information extraction have been developed by many researchers, either using manual or automatic method, but still need to be improved performance related accuracy and speed of extraction. This research proposed an approach of information extraction that combines bootstrapping approach with Ontology-Based Information Extraction (OBIE). Bootstrapping approach using small seed of labelled data, is used to minimize human intervention on information extraction process, while the use of guide ontology for extracting classes, properties and instances, using for provide semantic content for semantic web. Combining both approaches expected to increase speed of extraction process and accuracy of extraction results. Case study to apply information extraction system using “LonelyPlanet” datasets. Keywords— Information extraction, ontology, bootstrapping, Ontology-Based Information Extraction, OBIE, performance

scholarly journals Japanese Rice Straw Community Approaches in Developing Rice Straw Culture (Case Study: Inagaki Wara No Kai, Aomori Prefecture, Japan)

2020 ◽  
Vol 5 (2) ◽  
pp. 113-120
Author(s):  
Pandu Purwanduru ◽  
◽  
Eka Permanasari ◽  
Akira Ueda ◽  
◽  
...  
Keyword(s):  
Research And Development ◽  
Everyday Life ◽  
Rice Straw ◽  
Wetland Rice ◽  
Global Issues ◽  
Special Events ◽  
Aomori Prefecture

The Japanese rice straw culture started from the Yayoi period, the start of wetland rice method of farming technique. The rice straw culture is spread across Japan, as the supply of the rice straw is high, and it does not require special tools to process it. The rice straw culture is performed both during the special events and everyday life. However, along with the modernization and industrialization of agriculture, the culture slowly disappears. It is increasingly difficult to find the rice straw culture in Japan. To prevent this, several rice straw communities create a movement to preserve the culture. Within their methods, the community focuses on pure preservation, preservation and development or pure development. An example of the community focusing on the preservation and development is the Inagaki Wara no Kai. With this method, this community help to preserve the traditional activities of Inagaki village while at the same time creating new events for wider community. The development is rooted in local and global issues and the process of preserving and developing the rice straw culture is documented through workshops, exhibition and festival. These activities are conducted in the cooperation with different stakeholders such as participants, research and development partners, facilitators, or sponsors.

scholarly journals Natural Language Processing (NLP) and Its Impact across Industries – Unlocking the True Potential of Digital Healthcare (A Case Study Approach)

2021 ◽  
pp. 86-98
Author(s):  
Sourajit Roy ◽  
Pankaj Pathak ◽  
S. Nithya
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Computational Models ◽  
M2m Communication ◽  
Digital Revolution ◽  
Language Complexity ◽  
Study Approach ◽  
Data Variability

During the advent of the 21st century, technical breakthroughs and developments took place. Natural Language Processing or NLP is one of their promising disciplines that has been increasingly dynamic via groundbreaking findings on most computer networks. Because of the digital revolution the amounts of data generated by M2M communication across devices and platforms such as Amazon Alexa, Apple Siri, Microsoft Cortana, etc. were significantly increased. This causes a great deal of unstructured data to be processed that does not fit in with standard computational models. In addition, the increasing problems of language complexity, data variability and voice ambiguity make implementing models increasingly harder. The current study provides an overview of the potential and breadth of the NLP market and its acceptance in industry-wide, in particular after Covid-19. It also gives a macroscopic picture of progress in natural language processing research, development and implementation.

scholarly journals “A point that escapes Darwin”: Crises of colonial self in the nature essays of Edward Hamilton Aitken and Philip Robinson

2021 ◽  
pp. 002198942110328
Author(s):  
Jason Sandhar
Keyword(s):  
Everyday Life ◽  
Civil Servants ◽  
Gender And Race ◽  
Daily Work ◽  
Psychic Life ◽  
British Rule ◽  
The Everyday

This article shows how the colonial nature essay both spoofs and affirms crises of the European self in British India’s post-Rebellion era (1857–1947). Authored by English civil servants who took to naturalism as a hobby, the nature essay’s exaggerated misadventures with quotidian animals such as ants, beetles, and mosquitos parody British accounts of the 1857 Rebellion, while dehumanizing caricatures of uncooperative servants reduce Indian society’s complex hierarchies of class, caste, gender, and race to buffoonery. Taking as a case study two of the genre’s exemplars, Edward Hamilton Aitken and Philip Robinson, I read the colonized animals and people in these texts as agents who destabilize the material and psychic life of empire. Historians and postcolonialists agree that censorship, paranoia, and violence defined British rule over India between 1857 and 1947, yet they overlook the everyday life of empire. The nature essay’s peculiar synthesis of humour and science grants surprising insights into how colonial agents understood themselves as Raj hegemony shifted into its final stages. As the nature essay’s colonized people and animals thwart the daily work of empire, they also reveal the colonial class’ failure to confront its anxieties about the sahib’s political and epistemic stability as a rational, post-Enlightenment agent destined to master the colony.

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