Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNatural Language Processing 4 All (NLP4All): A New Online Platform for Teaching and Learning NLP Concepts
معالجة اللغة الطبيعية 4 ALL (NLP4ALL): منصة جديدة عبر الإنترنت لتدريس وتعلم مفاهيم NLP
326
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
توفر معالجة اللغة الطبيعية رؤى جديدة في بيانات اللغة عبر جميع التخصصات والمجالات تقريبا، وتتيح لنا التأكيدات و / أو تحدي المعرفة الموجودة.الحواجز الأساسية لتوسيع مشاركة هذه الأدوات البحثية الجديدة واستخدامها هي، أولا، عدم وجود مهارات الترميز في الطلاب عبر K-16، وفي السكان ككل، وثانيا، عدم وجود معرفة بكيفية أساليب NLPأن تستخدم للإجابة على أسئلة الاهتمام التأديبي خارج اللغويات و / أو علوم الكمبيوتر.لتوسيع نطاق المشاركة في NLP وتحسين محو الأمية NLP، أدخلنا أداة قائمة على شبكة الإنترنت أداة جديدة تسمى معالجة اللغة الطبيعية 4 جميعها (NLP4ALL).الغرض المقصود من NLP4ALL هو مساعدة المعلمين على تسهيل التعلم مع NLP وحولها، من خلال توفير واجهات سهلة الاستخدام لأساليب NLP والبيانات والتحليلات، مما يجعل من الممكن للمبرمجين غير المبتدئين تعلم مفاهيم NLP بشكل تفاعلي.
Natural Language Processing offers new insights into language data across almost all disciplines and domains, and allows us to corroborate and/or challenge existing knowledge. The primary hurdles to widening participation in and use of these new research tools are, first, a lack of coding skills in students across K-16, and in the population at large, and second, a lack of knowledge of how NLP-methods can be used to answer questions of disciplinary interest outside of linguistics and/or computer science. To broaden participation in NLP and improve NLP-literacy, we introduced a new tool web-based tool called Natural Language Processing 4 All (NLP4All). The intended purpose of NLP4All is to help teachers facilitate learning with and about NLP, by providing easy-to-use interfaces to NLP-methods, data, and analyses, making it possible for non- and novice-programmers to learn NLP concepts interactively.
References used
https://aclanthology.org/
rate research
Read More
In this paper we present a new Massive Open Online Course on Natural Language Processing, targeted at non-English speaking students. The course lasts 12 weeks, every week consists of lectures, practical sessions and quiz assigments. Three weeks out o
f 12 are followed by Kaggle-style coding assigments. Our course intents to serve multiple purposes: (i) familirize students with the core concepts and methods in NLP, such as language modelling or word or sentence representations, (ii) show that recent advances, including pre-trained Transformer-based models, are build upon these concepts; (iii) to introduce architectures for most most demanded real-life applications, (iii) to develop practical skills to process texts in multiple languages. The course was prepared and recorded during 2020 and so far have received positive feedback.
Natural language processing (NLP) research combines the study of universal principles, through basic science, with applied science targeting specific use cases and settings. However, the process of exchange between basic NLP and applications is often
assumed to emerge naturally, resulting in many innovations going unapplied and many important questions left unstudied. We describe a new paradigm of Translational NLP, which aims to structure and facilitate the processes by which basic and applied NLP research inform one another. Translational NLP thus presents a third research paradigm, focused on understanding the challenges posed by application needs and how these challenges can drive innovation in basic science and technology design. We show that many significant advances in NLP research have emerged from the intersection of basic principles with application needs, and present a conceptual framework outlining the stakeholders and key questions in translational research. Our framework provides a roadmap for developing Translational NLP as a dedicated research area, and identifies general translational principles to facilitate exchange between basic and applied research.
Natural Language Processing (NLP) systems are at the heart of many critical automated decision-making systems making crucial recommendations about our future world. Gender bias in NLP has been well studied in English, but has been less studied in oth
er languages. In this paper, a team including speakers of 9 languages - Chinese, Spanish, English, Arabic, German, French, Farsi, Urdu, and Wolof - reports and analyzes measurements of gender bias in the Wikipedia corpora for these 9 languages. We develop extensions to profession-level and corpus-level gender bias metric calculations originally designed for English and apply them to 8 other languages, including languages that have grammatically gendered nouns including different feminine, masculine, and neuter profession words. We discuss future work that would benefit immensely from a computational linguistics perspective.
This article explores the potential for Natural Language Processing (NLP) to enable a more effective, prevention focused and less confrontational policing model that has hitherto been too resource consuming to implement at scale. Problem-Oriented Pol
icing (POP) is a potential replacement, at least in part, for traditional policing which adopts a reactive approach, relying heavily on the criminal justice system. By contrast, POP seeks to prevent crime by manipulating the underlying conditions that allow crimes to be committed. Identifying these underlying conditions requires a detailed understanding of crime events - tacit knowledge that is often held by police officers but which can be challenging to derive from structured police data. One potential source of insight exists in unstructured free text data commonly collected by police for the purposes of investigation or administration. Yet police agencies do not typically have the skills or resources to analyse these data at scale. In this article we argue that NLP offers the potential to unlock these unstructured data and by doing so allow police to implement more POP initiatives. However we caution that using NLP models without adequate knowledge may either allow or perpetuate bias within the data potentially leading to unfavourable outcomes.
There are thousands of papers about natural language processing and computational linguistics, but very few textbooks. I describe the motivation and process for writing a college textbook on natural language processing, and offer advice and encouragement for readers who may be interested in writing a textbook of their own.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
