Home
Search results “Natural language interpretation”
Natural Language Processing: Crash Course Computer Science #36
 
11:50
Today we’re going to talk about how computers understand speech and speak themselves. As computers play an increasing role in our daily lives there has been an growing demand for voice user interfaces, but speech is also terribly complicated. Vocabularies are diverse, sentence structures can often dictate the meaning of certain words, and computers also have to deal with accents, mispronunciations, and many common linguistic faux pas. The field of Natural Language Processing, or NLP, attempts to solve these problems, with a number of techniques we’ll discuss today. And even though our virtual assistants like Siri, Alexa, Google Home, Bixby, and Cortana have come a long way from the first speech processing and synthesis models, there is still much room for improvement. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 152988 CrashCourse
Natural Language Processing in Artificial Intelligence in Hindi | NLP Easy Explanation
 
07:47
Hello Friends Welcome to Well Academy In this video i am Explaining Natural Language Processing in Artificial Intelligence in Hindi and Natural Language Processing in Artificial Intelligence is explained using an Practical Example which will be very easy for you to understand. Artificial Intelligence lectures or you can say tutorials are explained by Abdul Sattar Another Channel Link for Interesting Videos : https://www.youtube.com/channel/UCnKlI8bIoRdgzrPUNvxqflQ Google Duplex video : https://www.youtube.com/watch?v=RPOAz48uEc0 Sample Notes Link : https://goo.gl/KY9g2e For Full Notes Contact us through Whatsapp : +91-7016189342 Form For Artificial Intelligence Topics Request : https://goo.gl/forms/suL3639o2TG8aKkG3 Artificial Intelligence Full Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fug7z_YlD9M0x8gdZ7ziXen DBMS Gate Lectures Full Course FREE Playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fs6JyD7FFw9Ou1u601tev2D Computer Network GATE Lectures FREE playlist : https://www.youtube.com/playlist?list=PL9zFgBale5fsO-ui9r_pmuDC3d2Oh9wWy Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 20444 Well Academy
Padmaja Bhagwat - Listen, Attend, and Walk : Interpreting natural language navigational instructions
 
27:42
Speaker: Padmaja Bhagwat Imagine you have an appointment in a large building you do not know. Your host sent instructions describing how to reach their office. Though the instructions were fairly clear, in a few places, such as at the end, you had to infer what to do. How does a _robot (agent)_ interpret an instruction in the environment to infer the correct course of action? Enabling harmonious _Human - Robot Interaction_ is of primary importance if they are to work seamlessly alongside people. Dealing with natural language instructions in hard because of two main reasons, first being, Humans - through their prior experience know how to interpret natural language but agents can’t, and second is overcoming the ambiguity that is inherently associated with natural language instructions. This talk is about how deep learning models were used to solve such complex and ambiguous problem of converting natural language instruction into its corresponding action sequence. Following verbal route instructions requires knowledge of language, space, action and perception. In this talk I shall be presenting, a neural sequence-to-sequence model for direction following, a task that is essential to realize effective autonomous agents. At a high level, a sequence-to- sequence model is an end-to-end model made up of two recurrent neural networks: - **Encoder** - which takes the model’s input sequence as input and encodes it into a fixed-size context vector. - **Decoder** - which uses the context vector from above as a seed from which to generate an output sequence. For this reason, sequence-to-sequence models are often referred to as _encoder-decoder_ models. The alignment based encoder-decoder model would translate the natural language instructions into corresponding action sequences. This model does not assume any prior linguistic knowledge: syntactic, semantic or lexical. The model learns the meaning of every word, including object names, verbs, spatial relations as well as syntax and the compositional semantics of the language on its own. In this talk, steps involved in pre-processing of data, training the model, testing the model and final simulation of the model in the virtual environment will be discussed. This talk will also cover some of the challenges and trade-offs made while designing the model. Slides can be found at: https://speakerdeck.com/pycon2018 and https://github.com/PyCon/2018-slides
Views: 440 PyCon 2018
Adding meaning to natural language processing
 
39:17
My talk at O’Reilly AI in New York on adding meaning to natural language processing (NLP). I give an overview of both deep learning and symbolic methods for NLP.
Views: 308 Jonathan Mugan
Interpreting Language Using the Natural Language Classifier
 
36:27
What is the Natural Language Classifier API? How can it enhance my current app development efforts? Join Rahul Garg, a Strategy and Offering Manager focused on NLC, for a discussion around this unique API. Then receive specific examples on how to train and call the classifier and get your use case or technical questions answered.
Views: 2243 IBM Watson
Natural Language Processing (NLP) Tutorial - Introduction to Natural Language Processing
 
13:32
Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com Natural Language Processing is a way for computers to analyze, understand and derive meaning from human language. It was invented in the 1950s as a response to the global threat of the Cold War and has advanced greatly in the past decades. In this NLP Tutorial, we give an overview of Natural Language Processing: what it is, how it works, and what methods it uses in order to understand human language. Watch this video to learn: - The different subfields of NLP - What is the main obstacle NLP faces - NLP's potential for the future
Views: 1520 Fullstack Academy
Lecture 7 — Linguistics - Natural Language Processing | University of Michigan
 
21:28
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Natural Language Understanding: Foundations and State-of-the-Art
 
01:31:01
Percy Liang, Stanford University https://simons.berkeley.edu/talks/percy-liang-01-27-2017-1 Foundations of Machine Learning Boot Camp
Views: 14247 Simons Institute
Lecture 23 — Parsing - Natural Language Processing | University of Michigan
 
17:13
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Talking about Natural Language Processing
 
01:07
A brief synopsis of the Natural Language Processing concentration from Term II of Udacity's Artificial Intelligence Nanodegree www.udacity.com/ai
Views: 4342 Udacity
AWS re:Invent 2017: Natural Language Processing Plus Natural Language Generation: Th (ALX322)
 
39:10
Your Alexa skill could become the voice of your company to customers. How do you make sure that it conveys rich information, delivered with your brand's personality? In this session, Adam Long, VP of Product Management at Automated Insights, discusses natural language generation (NLG) techniques and how to make your Alexa response more insightful and engaging. Rob McCauley, Solutions Architect with Amazon Alexa, shows you how to put those techniques into action.
Views: 1110 Amazon Web Services
Natural Language Annotation for Machine Learning
 
19:49
Learn why it's important to create your own corpus and different methods to do so from the authors of "Natural Language Annotation for Machine Learning." Amber and James also go over their MATTER development process, and what they see coming next in the machine learning field.
Views: 3948 O'Reilly
The Basics of Natural Language Processing
 
04:11
Not sure what natural language processing is and how it applies to you? In this video, we lay out the basics of natural language processing so you can better understand what it is, how it works, and how it's being used in the real world today. To learn more on how SparkCognition is taking artificial intelligence into the real world, check out our DeepNLP solution: http://bit.ly/2JA74Cq
Views: 7719 SparkCognition
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
19:54
Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 394674 sentdex
Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka
 
08:26
** Natural Language Processing Using Python: https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry. NLP Tutorial : https://www.youtube.com/watch?v=05ONoGfmKvA Subscribe to our channel to get video updates. Hit the subscribe button above. ------------------------------------------------------------------------------------------------------- #NLPin10minutes #NLPtutorial #NLPtraining #Edureka Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ ------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learned content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For Natural Language Processing Training call us at US: +18336900808 (Toll Free) or India: +918861301699 , Or, write back to us at [email protected]
Views: 7543 edureka!
What is NATURAL LANGUAGE? What does NATURAL LANGUAGE mean? NATURAL LANGUAGE meaning & explanation
 
04:18
What is NATURAL LANGUAGE? What does NATURAL LANGUAGE mean? NATURAL LANGUAGE meaning - NATURAL LANGUAGE definition - NATURAL LANGUAGE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In neuropsychology, linguistics and the philosophy of language, a natural language or ordinary language is any language that has evolved naturally in humans through use and repetition without conscious planning or premeditation. Natural languages can take different forms, such as speech, signing, or writing. They are distinguished from constructed and formal languages such as those used to program computers or to study logic. Though the exact definition varies between scholars, natural language can broadly be defined in contrast to artificial or constructed languages (such as computer programming languages and international auxiliary languages) and to other communication systems in nature. Such examples include bees' waggle dance and whale song, to which researchers have found and/or applied the linguistic cognates of dialect and even syntax. All language varieties of world languages are natural languages, although some varieties are subject to greater degrees of published prescriptivism and/or language regulation than others. Thus nonstandard dialects can be viewed as a wild type in comparison with standard languages. But even an official language with a regulating academy, such as Standard French with the French Academy, is classified as a natural language (for example, in the field of natural language processing), as its prescriptive points do not make it either constructed enough to be classified as a constructed language or controlled enough to be classified as a controlled natural language. Controlled natural languages are subsets of natural languages whose grammars and dictionaries have been restricted in order to reduce or eliminate both ambiguity and complexity (for instance, by cutting down on rarely used superlative or adverbial forms or irregular verbs). The purpose behind the development and implementation of a controlled natural language typically is to aid non-native speakers of a natural language in understanding it, or to ease computer processing of a natural language. An example of a widely used controlled natural language is Simplified English, which was originally developed for aerospace industry maintenance manuals. Constructed international auxiliary languages such as Esperanto and Interlingua (even those that have native speakers) are not generally considered natural languages. Natural languages have been used to communicate and have evolved in a natural way, whereas Esperanto was designed by L.L. Zamenhof selecting elements from natural languages, not grown from natural fluctuations in vocabulary and syntax. Some natural languages have become naturally "standardized" by children's natural tendency to correct for illogical grammatical structures in their parents' speech, which can be seen in the development of pidgin languages into creole languages (as explained by Steven Pinker in The Language Instinct), but this is not the case in many languages, including constructed languages such as Esperanto, where strict rules are in place as an attempt to consciously remove such irregularities. The possible exception to this are true native speakers of such languages. More substantive basis for this designation is that the vocabulary, grammar, and orthography of Interlingua are natural; they have been standardized and presented by a linguistic research body, but they predated it and are not themselves considered a product of human invention. Most experts, however, consider Interlingua to be naturalistic rather than natural. Latino Sine Flexione, a second naturalistic auxiliary language, is also naturalistic in content but is no longer widely spoken.
Views: 306 The Audiopedia
What is NATURAL LANGUAGE PROCESSING? What does NATURAL LANGUAGE PROCESSING mean?
 
09:41
What is NATURAL LANGUAGE PROCESSING? What does NATURAL LANGUAGE PROCESSING mean? NATURAL LANGUAGE PROCESSING meaning - NATURAL LANGUAGE PROCESSING definition - NATURAL LANGUAGE PROCESSING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Challenges in natural language processing frequently involve natural language understanding, natural language generation (frequently from formal, machine-readable logical forms), connecting language and machine perception, dialog systems, or some combination thereof. The history of NLP generally started in the 1950s, although work can be found from earlier periods. In 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence. The Georgetown experiment in 1954 involved fully automatic translation of more than sixty Russian sentences into English. The authors claimed that within three or five years, machine translation would be a solved problem. However, real progress was much slower, and after the ALPAC report in 1966, which found that ten-year-long research had failed to fulfill the expectations, funding for machine translation was dramatically reduced. Little further research in machine translation was conducted until the late 1980s, when the first statistical machine translation systems were developed. Some notably successful NLP systems developed in the 1960s were SHRDLU, a natural language system working in restricted "blocks worlds" with restricted vocabularies, and ELIZA, a simulation of a Rogerian psychotherapist, written by Joseph Weizenbaum between 1964 and 1966. Using almost no information about human thought or emotion, ELIZA sometimes provided a startlingly human-like interaction. When the "patient" exceeded the very small knowledge base, ELIZA might provide a generic response, for example, responding to "My head hurts" with "Why do you say your head hurts?". During the 1970s, many programmers began to write "conceptual ontologies", which structured real-world information into computer-understandable data. Examples are MARGIE (Schank, 1975), SAM (Cullingford, 1978), PAM (Wilensky, 1978), TaleSpin (Meehan, 1976), QUALM (Lehnert, 1977), Politics (Carbonell, 1979), and Plot Units (Lehnert 1981). During this time, many chatterbots were written including PARRY, Racter, and Jabberwacky. Up to the 1980s, most NLP systems were based on complex sets of hand-written rules. Starting in the late 1980s, however, there was a revolution in NLP with the introduction of machine learning algorithms for language processing. This was due to both the steady increase in computational power (see Moore's law) and the gradual lessening of the dominance of Chomskyan theories of linguistics (e.g. transformational grammar), whose theoretical underpinnings discouraged the sort of corpus linguistics that underlies the machine-learning approach to language processing. Some of the earliest-used machine learning algorithms, such as decision trees, produced systems of hard if-then rules similar to existing hand-written rules. However, part-of-speech tagging introduced the use of hidden Markov models to NLP, and increasingly, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to the features making up the input data. The cache language models upon which many speech recognition systems now rely are examples of such statistical models. Such models are generally more robust when given unfamiliar input, especially input that contains errors (as is very common for real-world data), and produce more reliable results when integrated into a larger system comprising multiple subtasks....
Views: 4285 The Audiopedia
Natural Language Processing (NLP) Tutorial | Data Science Tutorial | Simplilearn
 
33:22
Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora. Python for Data Science Certification Training Course: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Science-NLP-6WpnxmmkYys&utm_medium=SC&utm_source=youtube The Data Science with Python course is designed to impart an in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The course is packed with real-life projects, assignment, demos, and case studies to give a hands-on and practical experience to the participants. Mastering Python and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization. Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it's modeling, and implementation using SAS. As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis. Who should take this course? There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. Analytics professionals who want to work with Python 2. Software professionals looking for a career switch in the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in Analytics and Data Science 5. Experienced professionals who would like to harness data science in their fields 6. Anyone with a genuine interest in the field of Data Science For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 20182 Simplilearn
Deep Learning for Natural Language Processing
 
20:07
Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations.
Views: 9908 Machine Learning TV
Introduction to Natural Language Processing - Cambridge Data Science Bootcamp
 
22:23
Talk by Ekaterina Kochmar, University of Cambridge, at the Cambridge Coding Academy Data Science Bootcamp: https://cambridgecoding.com/datascience-bootcamp
Views: 128193 Cambridge Coding Academy
Natural Language Processing (NLP) Tutorial with Python & NLTK
 
38:10
This video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and more. Python, NLTK, & Jupyter Notebook are used to demonstrate the concepts. This tutorial was developed by Edureka. 🔗NLP Certification Training: https://goo.gl/kn2H8T 🔗Subscribe to the Edureka YouTube channel: https://www.youtube.com/user/edurekaIN 🔗Edureka Online Training: https://www.edureka.co/ -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
Views: 5695 freeCodeCamp.org
What is NATURAL LANGUAGE PROGRAMMING? What does NATURAL LANGUAGE PROGRAMMING mean?
 
05:13
What is NATURAL LANGUAGE PROGRAMMING? What does NATURAL LANGUAGE PROGRAMMING mean? NATURAL LANGUAGE PROGRAMMING meaning - NATURAL LANGUAGE PROGRAMMING definition - NATURAL LANGUAGE PROGRAMMING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Natural language programming (NLP) is an ontology-assisted way of programming in terms of natural language sentences, e.g. English. A structured document with Content, sections and subsections for explanations of sentences forms a NLP document, which is actually a computer program. Natural languages and natural language user interfaces include Inform7, a natural programming language for making interactive fiction, Ring, a general purpose language, Shakespeare, an esoteric natural programming language in the style of the plays of William Shakespeare, and Wolfram Alpha, a computational knowledge engine, using natural language input. The smallest unit of statement in NLP is a sentence. Each sentence is stated in terms of concepts from the underlying ontology, attributes in that ontology and named objects in capital letters. In an NLP text every sentence unambiguously compiles into a procedure call in the underlying high-level programming language such as MATLAB, Octave, SciLab, Python, etc. Symbolic languages such as Mathematica are capable of interpreted processing of queries by sentences. This can allow interactive requests such as that implemented in Wolfram Alpha. The difference between these and NLP is that the latter builds up a single program or a library of routines that are programmed through natural language sentences using an ontology that defines the available data structures in a high level programming language. Natural language programming is a top down method of writing software. Its stages are as follows: Definition of an ontology - taxonomy - of concepts needed to describe tasks in the topic addressed. Each concept and all their attributes are defined in natural language words. This ontology will define the data structures the NLP can use in sentences. Definition of one or more top level sentences in terms of concepts from the ontology. These sentences are later used to invoke the most important activities in the topic. Defining of each of the top level sentences in terms of a sequence of sentences. Defining each of the lower level sentences in terms of other sentences or by a simple sentence of the form Execute code "...". where ... stands for a code in terms of the associated high level programming language. Repeating the previous step until you have no sentences left undefined. During this process each of sentences can be classified to belong to a section of the document to be produced in HTML or Latex format to form the final NLP program. Testing the meaning of each sentence by executing its code using testing objects. Providing a library of procedure calls (in the underlying high level language) which are needed in the code definitions of some low-level-sentence meanings. Providing a title, author data and compiling the sentences into an HTML or LaTex file. Publishing the NLP program as a webpage on the Internet or as a PDF file compiled from the LaTex document. An NLP program is a precise formal description of some procedure that its author created. It is human readable and it can also be read by a suitable software agent. For example, a web page in an NLP format can be read by a software personal assistant agent to a person and she or he can ask the agent to execute some sentences, i.e. carry out some task or answer a question. There is a reader agent available for English interpretation of HTML based NLP documents that a person can run on her personal computer . An ontology class in a natural language program that is not a concept in the sense as humans use concepts. Concepts in an NLP are examples (samples) of generic human concepts. Each sentence in an NLP program is either (1) stating a relationship in a world model or (2) carries out an action in the environment or (3) carries out a computational procedure or (4) invokes an answering mechanism in response to a question. A set of NLP sentences, with associated ontology defined, can also be used as a pseudo code that does not provide the details in any underlying high level programming language. In such an application the sentences used become high level abstractions (conceptualisations) of computing procedures that are computer language and machine independent.
Views: 603 The Audiopedia
What is NATURAL LANGUAGE UNDERSTANDING? What does NATURAL LANGUAGE UNDERSTANDING mean?
 
05:29
What is NATURAL LANGUAGE UNDERSTANDING? What does NATURAL LANGUAGE UNDERSTANDING mean? NATURAL LANGUAGE UNDERSTANDING meaning - NATURAL LANGUAGE UNDERSTANDING definition - NATURAL LANGUAGE UNDERSTANDING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Natural language understanding (NLU) is a subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU is considered an AI-hard problem. The process of disassembling and parsing input is more complex than the reverse process of assembling output in natural language generation because of the occurrence of unknown and unexpected features in the input and the need to determine the appropriate syntactic and semantic schemes to apply to it, factors which are pre-determined when outputting language. There is considerable commercial interest in the field because of its application to news-gathering, text categorization, voice-activation, archiving, and large-scale content-analysis. The umbrella term "natural language understanding" can be applied to a diverse set of computer applications, ranging from small, relatively simple tasks such as short commands issued to robots, to highly complex endeavors such as the full comprehension of newspaper articles or poetry passages. Many real world applications fall between the two extremes, for instance text classification for the automatic analysis of emails and their routing to a suitable department in a corporation does not require in depth understanding of the text, but is far more complex than the management of simple queries to database tables with fixed schemata. Throughout the years various attempts at processing natural language or English-like sentences presented to computers have taken place at varying degrees of complexity. Some attempts have not resulted in systems with deep understanding, but have helped overall system usability. For example, Wayne Ratliff originally developed the Vulcan program with an English-like syntax to mimic the English speaking computer in Star Trek. Vulcan later became the dBase system whose easy-to-use syntax effectively launched the personal computer database industry. Systems with an easy to use or English like syntax are, however, quite distinct from systems that use a rich lexicon and include an internal representation (often as first order logic) of the semantics of natural language sentences. Hence the breadth and depth of "understanding" aimed at by a system determine both the complexity of the system (and the implied challenges) and the types of applications it can deal with. The "breadth" of a system is measured by the sizes of its vocabulary and grammar. The "depth" is measured by the degree to which its understanding approximates that of a fluent native speaker. At the narrowest and shallowest, English-like command interpreters require minimal complexity, but have a small range of applications. Narrow but deep systems explore and model mechanisms of understanding, but they still have limited application. Systems that attempt to understand the contents of a document such as a news release beyond simple keyword matching and to judge its suitability for a user are broader and require significant complexity, but they are still somewhat shallow. Systems that are both very broad and very deep are beyond the current state of the art.
Views: 1126 The Audiopedia
Natural Language Processing in Python
 
01:51:03
Alice Zhao https://pyohio.org/2018/schedule/presentation/38/ Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. As a data scientist, I often use NLP techniques to interpret text data that I'm working with for my analysis. During this tutorial, I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn. ## Setup Instructions [ https://github.com/adashofdata/nlp-in-python-tutorial](https://github.com/adashofdata/nlp-in-python-tutorial) === https://pyohio.org A FREE annual conference for anyone interested in Python in and around Ohio, the entire Midwest, maybe even the whole world.
Views: 2259 PyOhio
How Can Computers Understand Human Language? | Natural Language Processing Explained
 
02:47
Natural language processing allows computers to understand human language. It has plenty of applications. For example: Text summarization, translation, keyword generation, sentiment analysis or chat bots. So how it works? Let’s take a closer look at it. Please Like and Subscribe for more weekly videos! Follow me on Twitter: https://twitter.com/thecompscirocks Follow me on Instagram: https://www.instagram.com/thecompscirocks/ Follow me on Facebook: https://www.facebook.com/thecompscirocks/ Some sources & further reading: http://www.mind.ilstu.edu/curriculum/protothinker/natural_language_processing.php https://nlp.stanford.edu/ https://research.google.com/pubs/NaturalLanguageProcessing.html https://en.wikipedia.org/wiki/Natural_language_processing https://en.wikipedia.org/wiki/Natural_language_understanding https://en.wikipedia.org/wiki/Natural_language_generation https://en.wikipedia.org/wiki/Tokenization_(lexical_analysis) https://en.wikipedia.org/wiki/Syntax https://en.wikipedia.org/wiki/Parsing https://en.wikipedia.org/wiki/Context-free_grammar https://en.wikipedia.org/wiki/Semantics https://en.wikipedia.org/wiki/Pragmatics https://en.wikipedia.org/wiki/Sentiment_analysis
Views: 6562 CSRocks
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Training | Edureka
 
40:29
** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics. The following topics covered in this video : 1. The Evolution of Human Language 2. What is Text Mining? 3. What is Natural Language Processing? 4. Applications of NLP 5. NLP Components and Demo Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV --------------------------------------------------------------------------------------------------------- Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ --------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learnt content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For Natural Language Processing Training call us at US: +18336900808 (Toll Free) or India: +918861301699 , Or, write back to us at [email protected]
Views: 5278 edureka!
Natural Language Interpreter Demo
 
02:06
This is a demo of the natural language interpreter a couple of friends and I built for the final project of my Information Retrieval class, and how it integrates into Automaton. It takes an English sentence, extracts relevant words from it, and converts those words into a command + arguments to pass into Automaton. Ignore the "translate" screw up at the end, apparently Google is heavily rate-limiting translations, and I used up all of mine testing the command before filming...
Views: 195 AutomatonServer
Sentiment Analysis in 4 Minutes
 
04:51
Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 88403 Siraj Raval
Artificial Intelligence (AI) and Natural Language Processing (NLP)  Made Easy
 
01:00:01
Artificial Intelligence (AI) and Natural Language Processing (NLP) are more than technologies, they are revolutions in how the world works. Beyond being the voice on our smartphones that tells us who was the 12th president of the United States (which is Zachary Taylor, by the way), AI helps farmers grow crops, doctors treat patients, financial advisors choose stocks, and driverless cars stay on the road. If it is not in a particular vertical industry today, it will be tomorrow. This webinar introduces you to a few of the basics constructs of AI and NLP, while showing you how offerings from AI/NLP providers such as Facebook, Amazon, Google, and IBM can be integrated into your enterprise via Avaya Breeze workflows.
Tomorrow’s Natural Language Understanding (NLU) // Omer Biran, Recast.AI
 
30:50
Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark Capital.
Views: 452 Data Driven NYC
Lecture 10 — Morphology and the Lexicon - Natural Language Processing | Michigan
 
20:21
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Lecture 68 — Semantics | Natural Language Processing | University of Michigan
 
06:56
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
How Complex is Natural Language? The Chomsky Hierarchy
 
09:39
How can we describe the complexity of linguistic systems? Where does natural language fit in? In this week's episode, we talk about the Chomsky hierarchy: what it captures, what characterizes different kinds of grammars on the hierarchy, and whether we can find grammars that sit higher on the scale than human language. This is Topic #63! This week's tag language: Croatian! Related topics: Happy Little Trees: X' Theory - https://youtu.be/7UOcoQr0hvg Trace Evidence: Syntactic Movement - https://youtu.be/x5iBbSkp8rk Last episode: Quantifying Sets and Toasters: Generalized Quantifiers - https://youtu.be/U1l3C_hmjqM Other of our syntax and morphology videos: Raising the Bar: Raising and Control Verbs - https://youtu.be/SYoYNeaSYrU Organizing Meanings: Morphological Typology - https://youtu.be/Ts2DS0ZsTyo Referential Treatment: Binding Theory - https://youtu.be/9sqm_cex4kA Also, if you'd like to know more about the Chomsky Hierarchy and its impact on computer science, Computerphile's had a few videos about them: - Their episode on the hierarchy: https://www.youtube.com/watch?v=224plb3bCog. - Their episode about finite-state machines: https://www.youtube.com/watch?v=vhiiia1_hC4. - And their episode about how finite-state machines relate to grammar: https://www.youtube.com/watch?v=RjOCRYdg8BY. Find us on all the social media worlds: Tumblr: http://thelingspace.tumblr.com/ Twitter: http://twitter.com/TheLingSpace Facebook: http://www.facebook.com/thelingspace/ And at our website, http://www.thelingspace.com/ ! You can also find our store at the website, https://thelingspace.storenvy.com/ Our website also has extra content about this week's topic at http://www.thelingspace.com/episode-63/ We also have forums to discuss this episode, and linguistics more generally. Sources: (1) I-Language (1st Edition, Daniela isac & Charles Reiss) (2) Introduction to the Theory of Computation (3rd Edition, Michael Sipser) (3) Mathematical Logic for Computer Science (3rd Edition, Mordechai Ben-Ari) (4) Evidence Against the Context-Freeness of Natural Language (Stuart Shieber - http://www.eecs.harvard.edu/~shieber/Biblio/Papers/shieber85.pdf) (5) https://en.wikipedia.org/wiki/Chomsky_hierarchy (6) Syntactic Structures (Noam Chomsky) (7) Mathematical Methods in Linguistics (Barbara Partee, Alice G. ter Meulen, Robert Wall) Proof regarding crossing dependencies (adapted from the first edition of Introduction to Automata Theory, Languages, and Computation, by John Hopcroft and Jeffrey Ullman. Note where carets appear that the following character should be taken as superscript): We first capture the general pattern of embedded clauses in Swiss German with the language a^nb^mc^nd^m . We then treat this as the result of intersecting Swiss German with the regular language a*b*c*d*. Now, let L = {a^nb^mc^nd^m | n ≥ 1 and m ≥ 1}. Suppose L is a context-free language, and let p be the pumping length referred to in the pumping lemma for context-free languages (https://en.wikipedia.org/wiki/Pumping_lemma_for_context-free_languages). Consider the string z = a^pb^pc^pd^p. Let z = uvwxy satisfy the conditions of the pumping lemma. Then as |vwx| ≤ p, vx can contain at most two different symbols. Furthermore, if vx contains two different symbols, they must be consecutive, for example, a and b. If vx has only a’s, then uwy has fewer a’s than c’s and is not in L, a contradiction. We proceed similarly if vx consists of only b’s, only c’s, or only d’s. Now suppose that vx has a’s and b’s. Then uwy still has fewer a’s than c’s. A similar contradiction occurs if vx consist of b’s and c’s or c’s and d’s. Since these are the only possibilities, we conclude that L is not context-free. Since context-free languages are closed under intersection with regular languages, and the above intersection is not context-free, Swiss German must be non-context-free. Q.E.D. A proof of the pumping lemma itself can be found in Introduction to the Theory of Computation (among other places). For a discussion of the closure properties of context-free languages, see Mathematical Methods in Linguistics (among other places). Looking forward to next week!
Views: 13704 The Ling Space
Dan Jurafsky on Natural Language Processing with Jennifer John
 
04:54
Technovation 2016 Winner Jennifer John interviews Dan Jurafsky, Professor of Linguistics and Computer Science at Stanford University. Dan shares what first caught his interest in linguistics and computer science, and the intersection between artificial intelligence, language, sociology, and psychology.
Views: 842 Iridescent
Artificial Intelligence (AI) Approach towards Natural Language Understanding - Stephen Lernout, MIIA
 
12:32
This video was recorded at FTC 2016 - http://saiconference.com/FTC The problem being addressed in this paper is that using brute force in Natural Language Processing and Machine Learning combined with advanced statistics will only approximate meaning and thus will not deliver in terms of real text understanding. Counting words and tracking word order or parsing by syntax will also result in probability and guesswork at best. Their vendors struggle in delivering accurate quality and this results in ill-functioning applications. The newer generation methodologies like Deep Learning and Cognitive Computing are breaking barriers in the (Big Data) fields of Internet of Things, Robotics and Image/Video Recognition but cannot be successfully deployed for text without huge amounts of training and sample data. In the short term, we believe non-biological Artificial Intelligence will produce the best results for text understanding. Miia applied advanced Linguistic and Semantic Technologies combined with ConceptNet modeling and Machine Learning to successfully cater deep intelligent and cross-language quality to several industries.
Views: 1047 SAIConference
What Is Natural Language?
 
01:23
Natural languages and formal languages are very different. Inbenta CEO Jordi Torras explains the differences and how computers can be enabled to understand the way humans naturally talk. www.inbenta.com
Views: 1185 Inbenta
Introduction To Natural Language Processing In Artificial Intelligence (HINDI)
 
03:31
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
How Quantum Theory Can Help Understanding Natural Language
 
05:36
In this video I explain the basic ideas behind DIStributional COmpositional CATegorical (DisCoCat) models of meaning. These models are used in the Quantum Group (part of the Department of Computer Science, University of Oxford) to study how information flows between words in a sentence to give us the meaning of the sentence as a whole. For more information about this topic, see for example: Coecke, B., Sadrzadeh, M., & Clark, S. (2011). Mathematical Foundations for a Compositional Distributional Model of Meaning. Linguistic Analysis, 36(1–4), 345–384. For more information on how density matrices can model ambiguous words, see: Piedeleu, R., Kartsaklis, D., Coecke, B., & Sadrzadeh, M. (2015). Open System Categorical Quantum Semantics in Natural Language Processing. In Proceedings of the 6th Conference on Algebra and Coalgebra in Computer Science. MinutePhysics has been my great inspiration for making this video, check out the channel here: https://www.youtube.com/user/minutephysics The background music is Deliberate Thought by Kevin MacLeod and it is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/?keywords=deliberate+thought Artist: http://incompetech.com/ I also would like to thank Sjoerd Smit for writing a Mathematica script to crop all the images for this video. That has saved me countless hours of work. And of course, thank you, viewer, for watching this video. If you have any comments or funny examples of creative language use that would utterly confuse a computer, please let me know! Maaike Zwart
Views: 1236 DisCoCat
Natural Language Processing
 
02:03
A short explanation of how Natural Language Processing works and why the technology is important
Views: 950 Lucy Walsh
Lecture 16 — Thesaurus-based Word Similarity Methods - Natural Language Processing
 
07:39
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Lecture 44 — Hidden Markov Models (1/2) - Natural Language Processing | Michigan
 
24:42
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Using the Natural Language API with C#
 
01:01
Mete Atamel (@meteatamel) shows how you use the Natural Language API with C#. See the relevant codelab for more details: https://codelabs.developers.google.com/codelabs/cloud-natural-language-csharp
What is NATURAL LANGUAGE GENERATION? What does NATURAL LANGUAGE GENERATION mean?
 
08:02
What is NATURAL LANGUAGE GENERATION? What does NATURAL LANGUAGE GENERATION mean? NATURAL LANGUAGE GENERATION meaning - NATURAL LANGUAGE GENERATION definition - NATURAL LANGUAGE GENERATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Natural language generation (NLG) is the natural language processing task of generating natural language from a machine representation system such as a knowledge base or a logical form. Psycholinguists prefer the term language production when such formal representations are interpreted as models for mental representations. It could be said an NLG system is like a translator that converts data into a natural language representation. However, the methods to produce the final language are different from those of a compiler due to the inherent expressivity of natural languages. NLG has existed for a long time but commercial NLG technology has only recently become widely available. NLG may be viewed as the opposite of natural language understanding: whereas in natural language understanding the system needs to disambiguate the input sentence to produce the machine representation language, in NLG the system needs to make decisions about how to put a concept into words. Simple examples are systems that generate form letters. These do not typically involve grammar rules, but may generate a letter to a consumer, e.g. stating that a credit card spending limit was reached. To put it another way, simple systems use a template not unlike a Word document mail merge, but more complex NLG systems dynamically create text. As in other areas of natural language processing, this can be done using either explicit models of language (e.g., grammars) and the domain, or using statistical models derived by analysing human-written texts. The process to generate text can be as simple as keeping a list of canned text that is copied and pasted, possibly linked with some glue text. The results may be satisfactory in simple domains such as horoscope machines or generators of personalised business letters. However, a sophisticated NLG system needs to include stages of planning and merging of information to enable the generation of text that looks natural and does not become repetitive. The typical stages of natural language generation, as proposed by Dale and Reiter, are: Content determination: Deciding what information to mention in the text. For instance, in the pollen example above, deciding whether to explicitly mention that pollen level is 7 in the south east. Document structuring: Overall organisation of the information to convey. For example, deciding to describe the areas with high pollen levels first, instead of the areas with low pollen levels. Aggregation: Merging of similar sentences to improve readability and naturalness. For instance, merging the two sentences Grass pollen levels for Friday have increased from the moderate to high levels of yesterday and Grass pollen levels will be around 6 to 7 across most parts of the country into the single sentence Grass pollen levels for Friday have increased from the moderate to high levels of yesterday with values of around 6 to 7 across most parts of the country. Lexical choice: Putting words to the concepts. For example, deciding whether medium or moderate should be used when describing a pollen level of 4. ..
Views: 851 The Audiopedia
Natural Language Processing in Python: Part 1 -- Installation and Getting Started
 
22:13
In this video, we start off on our adventure into natural language processing with the Python. This will be the first of a multi-part series on the subject. Each video in this series will have a companion blog post, which covers the content of the video in greater detail, as well as a Github link to the Python code used. Both of these links are provided below: Blog Post: http://vprusso.github.io/blog/2018/natural-language-processing-python-1/ This video is part of a series on Natural Language Processing in Python. The link to the playlist may be accessed here: http://bit.ly/lp_nlp Python Code: https://github.com/vprusso/youtube_tutorials/blob/master/natural_language_processing/nlp_1.py If I've helped you, feel free to buy me a beer :) Bitcoin: 1CPDk4Hp4Fnh7tjeMdZBudmYAkCCcLqimT PayPal: https://www.paypal.me/VincentRusso1 Do you like the development environment I'm using in this video? It's a customized version of vim that's enhanced for Python development. If you want to see how I set up my vim, I have a series on this here: http://bit.ly/lp_vim If you've found this video helpful and want to stay up-to-date with the latest videos posted on this channel, please subscribe: http://bit.ly/lp_subscribe
Views: 1334 LucidProgramming
Natural Language Processing and Automated Speech Recognition for Data Analytics
 
19:09
Learn more at - https://amzn.to/2Hfy4FZ Gain insights in your customers and data by utilising machine learning techniques to analyse customer contact centers call recordings, translate them into different languages, and use them for further analysis of what drives positive outcomes. Using Amazon Transcribe for speech to text, Amazon Translate for language translation and Amazon Comprehend for insights in unstructured text. Speaker: Shaun Ray, Head of Solutions Architect, Amazon Web Services, ASEAN
Views: 660 Amazon Web Services
Lecture - 39 Natural Language Processing - I
 
57:29
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 84701 nptelhrd
Lecture 1 — Introduction - Natural Language Processing | University of Michigan
 
08:39
. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Dan Jurafsky on Natural Language Processing
 
05:25
Technovation 2016 Winner Jennifer John introduces Dan Jurafsky, Professor of Linguistics and Computer Science at Stanford University. Dan explains how natural language processing is transforming the way we interact with the world and understand ourselves.
Views: 2509 Iridescent
Steps in Natural Language Processing in Artificial Intelligence in HINDI | Artificial Intelligence
 
07:06
Hello Friends Welcome to Well Academy In this video i am Explaining Steps in Natural Language Processing in Artificial Intelligence in Hindi and Natural Language Processing in Artificial Intelligence is explained using an Practical Example which will be very easy for you to understand. Artificial Intelligence lectures or you can say tutorials are explained by Abdul Sattar Another Channel Link for Interesting Videos : https://www.youtube.com/channel/UCnKlI8bIoRdgzrPUNvxqflQ Natural Language Processing In artificial intelligence : https://youtu.be/IpBZ01g0pGE Google Duplex video : https://www.youtube.com/watch?v=RPOAz48uEc0 Sample Notes Link : https://goo.gl/KY9g2e For Full Notes Contact us through Whatsapp : +91-7016189342 Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 11830 Well Academy

Personal care assistant cover letter
Web content writing service
Dal newsletter formats
Drafting cover letter samples
School admission cover letter example