PowerPoint Presentation

[Audio] Hello, Welcome to the Lumenore certified NLQ analyst internship program Today, in this Course our goal is to focus on the Natural language query as part of this program, you will be learning about queries and insight into datasets..

[Audio] In this video, we can see how artificial intelligence converts words into meaning. This field of study that focuses on the interactions between human language and computers is called natural language query. It sits at the intersection of computer science, artificial intelligence, and computational linguistics. "Natural language query is a field that cov­ers com­puter un­der­stand­ing and ma­nip­u­la­tion of hu­man lan­guage, and it's ripe with pos­sib­il­it­ies for news­gath­er­ing," Anthony Pesce says in Natural Language Query. "You usu­ally hear about it in the con­text of ana­lyz­ing large pools of legis­la­tion or other doc­u­ment sets, at­tempt­ing to dis­cov­er pat­terns or root out cor­rup­tion." There are many applications for natural language Query, including business applications. This video covers everything you need to know about NLQ whether you're a developer, a business, or a complete beginner..

[Audio] NLQ is to help workers not accustomed to traditional, structured BI tools find insights and information they need to inform and make business decisions, while also being a helpful tool for advanced users throughout the enterprise. For non-technical analytics users, NLQ is an easy way to get detailed answers from their data faster than exploring a dashboard or chart and using it as a springboard for their ad-hoc reporting..

[Audio] Natural Language Query is a self-service BI tool that provides the ability for an analytics user to ask a question using non-technical language and get an automatically generated answer instantly. Using data modeling and machine learning technologies to parse questions for key terms, NLQ scans related databases and generates a tailored report or chart that provides relevant insight..

[Audio] The new approach is called guided natural language query or guided NLQ, which takes the concept of NLQ further by programming the solution itself to guide the user as they type a question, helping them structure it using pre-defined sequences and suggested prompts. This eliminates the burden of knowledge or any potential problems in accurately interpreting the semantics (language) of a user's question, as the system itself assists them in building it, from start to finish. They don't need prior technical knowledge or need to go to IT to get started finding answers from data. guided NLQ can help make the use of analytics more pervasive and open analytics for every type of businessperson in the enterprise—not just experts..

[Audio] The integration and implementation of natural language query capability, as well as the complexity of the questions and the types of data supported, varies between vendors. The most common and traditional approach is search-based NLQ, which places a free text search bar somewhere within the user interface (typically on a dashboard) that allows BI users to begin typing a question and get a list of reports and charts generated quickly as potential answers..

[Audio] Today, deep learning models and learning techniques based on convolutional neural networks ( CNNs) and recurrent neural networks ( RNNs) enable NLQ systems that 'learn' as they work and extract ever more accurate meaning from huge volumes of raw, unstructured, and unlabeled text and voice data sets. Imagine your business software speaks a foreign language that you're not fluent in – natural language query, or NLQ, is your translator. it takes your human input, reorganizes it, and explains what you've said in a way that your software can parse. using data modeling and machine learning technologies to parse questions for key terms, NLQ scans related databases and generates a tailored report or chart that provides relevant insight..

[Audio] Using data modeling and machine learning technologies to parse questions for key terms, NLQ scans related databases and generates a tailored report or chart that provides relevant insight..

[Audio] NLQ is used to analyze text, allowing machines to understand how humans speak. This human-computer interaction enables real-world applications like automatic text summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech tagging, relationship extraction, stemming, and more. NLQ is commonly used for text mining, machine translation, and automated question answering..

[Audio] NLQ is characterized as a difficult problem in computer science. human language is rarely precise or plainly spoken. To understand human language is to understand not only the words but the concepts and how they're linked together to create meaning. Despite the language being one of the easiest things for the human mind to learn, the ambiguity of language is what makes natural language queries a difficult problem for computers to master..

[Audio] NLQ algorithms have a variety of uses. basically, they allow developers and businesses to create software that understands human language. Due to the complicated nature of human language, NLQ can be difficult to learn and implement correctly. however, with the knowledge gained from this article, you will be better equipped to use NLQ successfully, no matter your use case..

[Audio] Summarize blocks of text using Summarizer to extract the most important and central ideas while ignoring irrelevant information then create a chatbot using " Parsey McParseface", a language parsing deep learning model made by google that uses point-of-speech tagging. then generate keyword topic tags from a document using LDA ( latent Dirichlet allocation), which determines the most relevant words from a document..

[Audio] This algorithm is at the heart of the Auto-Tag and Auto-Tag URL microservices, Identify the type of entity extracted, such as it is a person, place, or organization using named entity recognition. Sentiment Analysis, based on Stanford NLQ, can be used to identify the feeling, opinion, or belief of a statement, from very negative, to neutral, to very positive. Often, developers will use an algorithm to identify the sentiment of a term in a sentence or use sentiment analysis to analyze social media and reduce words to their root, or stem, using PorterStemmer, or break up text into tokens using Tokenizer..

[Audio] That's all about NLQ Thank you. A black background with white text Description automatically generated with low confidence.