Anyone who has used Google has already had experience with Natural Language Query (NLQ), often without realizing it. This article will take you through the world of NLQ, showing you how it works, what it can do, and what challenges it presents.

What is a Natural Language Query?

NLQ is a type of human data interaction where inquiries are made in natural, everyday language. Imagine being able to ask your computer a question as if you were speaking to another person, and receiving accurate answers.

History and Development of NLQ

Early Attempts

NLQ technology has existed in a simpler form since the early days of computer technology when scientists were trying to get machines to understand natural language.

Current Advances

However, in recent years, advances in artificial intelligence and machine learning have led to significant improvements in NLQ technology.

How NLQ Works

NLQ uses advanced algorithms and technologies to understand and process human language.

Technology Behind NLQ

Artificial Intelligence and Machine Learning

AI and machine learning help machines learn and adapt to the semantics of natural language.

Natural Language Processing (NLP)

NLP is the process through which computers can understand, interpret, and manipulate natural language.

Benefits of NLQ

NLQ technology offers numerous benefits, from improved user experience to increased productivity.

Improved User Experience

Easy to Use

NLQ allows users to ask questions without having to learn special data query languages.

Precise Results

With NLQ, users can get precise answers to specific questions by formulating their queries in natural language.

Productivity Increase

Efficient Data Analysis

Through NLQ, companies can use their data assets more efficiently, as NLQ delivers quick and accurate answers to data queries.

Accelerated Decision Making

With NLQ, decision-makers can make decisions faster and more informed by asking direct questions to their data.

Challenges and Limitations of NLQ

Despite its benefits, NLQ is not without challenges and limitations.

Ambiguity in Natural Language

Natural language is often ambiguous and can be difficult for machines to interpret.

Complexity of Data Integration

Integrating NLQ into existing data structures can be complex, especially in large companies with extensive data assets.

Future of NLQ

As NLQ technology continues to make advances, it is likely to play an increasingly larger role in how we interact with computers and analyze data.

Conclusion

In a world where data is increasingly at the heart of businesses and decision-making processes, NLQ has the potential to fundamentally change the way we handle data. While there are still challenges to overcome, the future of NLQ is promising and exciting.

Kind regards by GPT-5 (https://gpt5.blog/natural-language-query-nlq/)

Introduction to Natural Language Query (NLQ)

ضيفتي في حلقة اليوم من الموسم الثاني من البودكاست تجمَع بين المسار المهني في واحدة من كبرى شركات التوطين في مصر والوطن العربي، والمسار الأكاديمي بوصفها مدرسًا مُساعِدًا في العديد من الجامعات المصرية، منها جامعة بدر، والجامعة العربية للعلوم والتكنولوجيا والنقل البحري، وجامعة فاروس بالإسكندرية.

يُسعدني ويشرفني أن تكون ضيفتي في حلقة اليوم الدكتورة إيمان مجدي رفعت، مدرس مساعد بكلية اللغات والترجمة، جامعة فاروس بالإسكندرية، وباحثة دكتوراة في معهد الدراسات اللغوية والترجمة، كلية الآداب، جامعة الإسكندرية ، وقائدة فريق تطوير الأعمال بشركة أرابايز في الإسكندرية، واستشارية لغوية ومترجِمة ومُراجِعة ومقيِّمة جودة الترجمة الآلية مع عدة شركات دولية مثل Kayak وAgoda وArgos وRWS Moravia وLinguitronics وغيرها من الشركات.

تحدَّثنا في حلقة اليوم من البودكاست عن تحديات أدوات الترجمة بمساعدة الحاسوب لضعاف الإبصار، ومبادرتها لتمكينهم من مزايا هذه التقنيات. وتطرقنا أيضًا إلى أهمية منظومة العمل داخل شركات الترجمة والتوطين في توفير بيئة خصبة للدراسات الأكاديمية. واستعرضنا كذلك مفهوم تقييم الترجمة الآلية من وجهة نظرها كباحثة دكتوراة في هذا المجال، وغيرها من الموضوعات.
لا تبخل بتعليقك على الحلقة أو بطرح سؤالك أو استفسارك.

مشاركة الحلقة مع الزملاء من المهتمين بصناعة الترجمة والتوطين محل تقدير لي ولفريق العمل.

يمكنكم أيضًا الاستماع إلى الحلقة على منصاتنا الصوتية كلٍ من ساوند كلاود وأبل بودكاست وجوجل بودكاست وسبوتيفاي.

✅ رابط الملف التعريفي للدكتورة إيمان مجدي رفعت على لينكد إن:
https://www.linkedin.com/in/iman-refaat-5266121a

💻 رابط مشاهدة الحلقة على منصة يوتيوب:
https://youtu.be/XyYXNGZ8gBE

عن تحديات أدوات الترجمة بمساعدة الحاسوب لضعاف الإبصار وتقييم الترجمة الآلية - حوار مع د. إيمان رفعت

In this episode, podcast co-hosts Dr. Dwight Stoll and Dr. James Grinias talk with Dr. Farooq Wahab, Research Engineering Scientist at the University of Texas at Arlington. Wahab has interests and expertise in chiral separations, improving the green-ness of chromatographic separations, new detectors for chromatography such as microwave rotational spectroscopy, and new techniques for signal processing in chromatography, among other areas. He and several coauthors recently published a paper in Analytical Chemistry discussing their experiences using generative artificial intelligence (i.e. chatGPT) as a research assistant when working on tasks in measurement science ranging from extraction of rotational microwave spectra from free induction decay signals, to baseline removal from chromatographic data.

In the conversation we talk about the origins of this study, strengths and weaknesses of chatGPT as a research assistant in the analytical chemistry space, and the role of generative AI in chemistry teaching and learning. We also touch on tips and tricks also published by Wahab and coworkers for dealing with equations and symbol-heavy text when using generative AI tools. One very interesting application of these tricks is in the translation of relatively old (decades) papers that were published before English became the standard language for much of science publication.

Ep. 30: The Potential of Generative A.I. as a Research Assistant for Measurement Science

Interpreter's Voice podcast episode critically reviews the impact of machine translation (MT) on human interpreters. We explore MT’s strengths and limitations, especially in sensitive fields like healthcare and law, and discuss why human interpreters remain essential for context, confidentiality, and cultural understanding. With analogies, facts, and statistics, this episode dives into the challenges MT faces and why it works best as a tool rather than a replacement for skilled interpreters. Stay tuned for an insightful discussion on the evolving role of technology in language services!

Critique an Element of Culture: Machine Translation

Analytically Speaking, the podcast from LCGC and Spectroscopy magazines, addresses important issues in separation science and analytical spectroscopy. Topics include new analytical techniques, methods, and approaches; the latest trends; advances in instrument and software technology; practical solutions for specific applications; recent papers in the scientific literature and their applicability; challenges and solutions for data analysis and interpretation; analytical chemistry theory and fundamentals (from advanced research to tutorials and troubleshooting); and more. Our regular hosts are Dwight Stoll, PhD, a professor of chemistry at Gustavus Adolphus College in St. Peter, Minnesota, and Jerry Workman, PhD, a spectroscopist, noted author, and currently the Senior Technical Editor of Spectroscopy and LCGC. Dwight covers separation science and Jerry addresses spectroscopy related topics.

This podcast discusses the capabilities of GPT-5, a next-generation AI system, and its potential impacts on medicine, finance, education, the economy, work, security, and society ...

أول بودكاست عربي عن الترجمة والتوطين والعمل المستقل في قطاع الخدمات اللغوية. أتحدث في هذا البودكاست عن الترجمة والتوطين من منظور قطاع الأعمال، فضلاً أهمية تطوير المترجم لمهاراته اللغوية والتقنية والشخصية، بجانب مهاراته في إدارة مشروعات الترجمة وإدارة الوقت والإدارة المالية وغيرها من المهارات. هذا البودكاست يقدمه محمد سعيد كامل، مترجم معتمد ومُحاضر بمجال الترجمة والتوطين وتقنيات الترجمة بمساعدة الحاسوب، بخبرة تزيد على 14 عامًا في المجال.

New tracks tagged #machinetranslation