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: 184364 CrashCourse
** 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 more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 37676 edureka!
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: 58790 Well Academy
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: 17587 PyOhio
. 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. .
Views: 1769 Artificial Intelligence - All in One
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: 1330 Amazon Web Services
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: 7519 CSRocks
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: 911 The Audiopedia
Navid Yaghmazadeh, Yuepeng Wang, Isil Dillig, Thomas Dillig This paper presents a new technique for automatically synthesizing SQL queries from natural language (NL). At the core of our technique is a new NL-based program synthesis methodology that combines semantic parsing techniques from the NLP community with type-directed program synthesis and automated program repair. Starting with a program sketch obtained using standard parsing techniques, our approach involves an iterative refinement loop that alternates between probabilistic type inhabitation and automated sketch repair. We use the proposed idea to build an end-to-end system called SQLIZER that can synthesize SQL queries from natural language. Our method is fully automated, works for any database without requiring additional customization, and does not require users to know the underlying database schema. We evaluate our approach on over 450 natural language queries concerning three different databases, namely MAS, IMDB, and YELP. Our experiments show that the desired query is ranked within the top 5 candidates in close to 90% of the cases and that SQLIZER outperforms NALIR, a state-of-the-art tool that won a best paper award at VLDB'14.
Views: 1344 Splash Conference 2017
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: 1108 The Audiopedia
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: 412 The Audiopedia
Invited talk, given by Michael Ernst (University of Washington, USA). A powerful, but limited, way to view software is as source code alone. Treating a program as a sequence of instructions enables it to be formalized and makes it amenable to mathematical techniques such as abstract interpretation and model checking. This is a limited way to view a program, because a program consists of much more than a sequence of instructions. Developers make use of test cases, documentation, variable names, program structure, the version control repository, and more. I argue that it is time to take the blinders off of software analysis tools: tools should use all these artifacts to deduce more powerful and useful information about the program. Researchers are beginning to make progress towards this vision. This paper gives, as examples, four results that find bugs and generate code by applying Natural Language Processing to English that appears in or with source code. - Comparing observed error messages to text in the user manual to determine whether the error message is adequate. - Using dictionary similarity among variable names to discover when variables are used incorrectly. - Creating test oracles (assert statements) from English descriptions of behavior (Javadoc documentation). - Translating user queries into executable bash commands. The four techniques use four different NLP techniques: document similarity, word semantics, parse trees, and neural networks. Initial results suggest that by paying attention to these rich sources of information, we can produce better software analysis tools and make programmers more productive. Many more opportunities exist, and I urge the community to grasp them. This is joint work with Arianna Blasi, Juan Caballero, Sergio Delgado Castellanos, Alberto Goffi, Alessandra Gorla, Victoria Lin, Deric Pang, Mauro Pezzè, Irfan Ul Haq, Kevin Vu, Luke Zettlemoyer, and Sai Zhang.
Views: 354 ETAPS 2017
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: 1410 The Audiopedia
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: 17284 SparkCognition
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: 196 AutomatonServer
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: 510 PyCon 2018
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: 449630 sentdex
** 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 more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 35331 edureka!
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: 2520 IBM Watson
In this video we will understand the detailed explanation of Lemmatization and understand how it can be used in Natural Language Processing. We will also see the basic difference between Lemmatization and stemming. Github link:https://github.com/krishnaik06/Natural-Language-Processing/blob/master/Lemmatization.py NLP playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm
Views: 287 Krish Naik
Mike Conover, Principal Data Scientist, Workday — This talk offers an overview of essential tools and techniques for natural language processing with a focus on approaches you can use straight away to build impactful machine learning technologies. From off-the-shelf neural models that capture the essence of a document to scrappy libraries that do far more than their share of heavy lifting, this practical session will help you cut to the chase when you need to research, build and ship on a startup timeline. We'll also outline an architectural foundation that makes the most of containerization and GPU compute alongside battle-hardened storage and parallelization patterns that have won the day time and time again. Taken together, these practical approaches to building natural language understanding systems will position you to start solving tough problems that will have a direct impact on your work. Mike Conover builds machine learning technologies that leverage the behaviors of hundreds of millions of people to model the flow of information through society. A principal data scientist at Workday, Mike previously lead news relevance engineering for the LinkedIn feed and machine learning research and development for SkipFlag, the smart knowledge graph that builds itself using deep learning (acquired by Workday.) Mike's work has been featured in the New York Times, the Wall Street Journal and on NPR.
Views: 451 Berkeley School of Information
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: 13059 Machine Learning TV
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: 1820 Fullstack Academy
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.
Views: 917 ASI is now ConvergeOne
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: 3497 Iridescent
We’re training machines to read and understand words in threat data in multiple languages and at unparalleled scale. Our technology automatically highlights attack methods, targets, vulnerabilities, and exploits — cutting through the noise to deliver relevant threat intelligence. Explore our YouTube channel to learn more or visit us at https://www.recordedfuture.com/technology/.
Views: 496 RecordedFuture
http://AutomatedInsights.com Discover how natural language generation technology is providing new opportunities for writing automation in business, significantly increasing productivity, efficiency and personalization across a range of industries. Outlining new practical applications for using natural language generation technology to automate personalized content for clients, prospects, shoppers and internal audiences. Hosted by: Robbie Allen, CEO & Founder of Automated Insights @RobbieAllen Hilary Mason, Founder of Fast Forward Labs @hmason
Views: 2477 Automated Insights
Rama Vedantam: Title: "Connecting Vision and Language for Interpretation, Grounding and Imagination" Abstract: Understanding how to model vision and language jointly is a long-standing challenge in artificial intelligence. Vision is one of the primary sensors we use to perceive the world, while language is our data structure to represent and communicate knowledge. In this talk, we will take up three lines of attack to this problem: interpretation, grounding, and imagination. In interpretation, the goal will be to get machine learning models to understand an image and describe its contents using natural language in a contextually relevant manner. In grounding, we will connect natural language to referents in the physical world, and show how this can help learn common sense. Finally, we will study how to ‘imagine’ visual concepts completely and accurately across the full range and (potentially unseen) compositions of their visual attributes. We will study these problems from computational as well as algorithmic perspectives and suggest exciting directions for future work.
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Views: 5064 Artificial Intelligence - All in One
Description How to automatically detect hate speech? Identify the author of a book? Search billions of web pages to give you the one you are looking for? The talk focuses on the practical side of natural language processing – from side projects to web-scale production systems. It discusses common pitfalls, shares lessons learned (the hard way) and proposes best practices. May contain bad puns. Abstract While there is plenty of good resources available to learn the theoretical side of natural language processing, the gap between theory and practice may be a hard one to bridge. This talk focuses on the practical side by presenting several examples of using Python and deep learning for natural language processing – from tiny projects to large production systems. The author shares common mistakes, important lessons, and best practices. Whether you're a newcomer to the field looking for tips on getting started or an experienced practitioner trying to improve your workflow, this talk will give you ideas for new projects, introduce you to modern techniques, and most importantly – let you avoid the mistakes of at least one other person. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 8014 PyData
Welcome to part 4 of the Google Cloud tutorial series. In this part, we're going to explore some of the Natural Language API. We're going to focus on the entity recognition and sentiment analysis, but you can also do syntactical analysis with this API. As usual, you will need to both enable this API and of course have the API credentials setup as we did in Part 2. From here, things should begin to look familiar with the APIs, for example we'll have client = language.Client(), and then we'll get all sorts of methods that we can do with some input, which, in this case, will be text. Sample code: https://pythonprogramming.net/natural-language-api-google-cloud-tutorial/ https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 42134 sentdex
How is AI transforming business right now? Using natural language processing, EY created a tool called EY Document Intelligence for Contract Review to improve the speed and accuracy of interpreting masses of contracts, allowing EY audit teams to focus on higher value activities.
Views: 1126 EY Global
Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com Natural language processing (NLP) is a field of computer science concerned with the interactions between computers and natural languages. In this NLP Tutorial, we explore the intersection of natural human language and the tech industry through Natural Language Processing. We talk about some of the tasks of NLP and tools available to developers to take advantage of the field's insights. Finally, we briefly introduce Wit.ai and explain how you can use it in your apps. Watch this video to learn: - What is Natural Language Processing - Why is it important for tech - What is Wit.ai and how does it use NLP
Views: 1909 Fullstack Academy
This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines. - Natural Language Processing (Part 1): Introduction to NLP & Data Science - Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python - Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python - Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python - Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python - Natural Language Processing (Part 6): Text Generation with Markov Chains in Python All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial
Views: 384 Alice Zhao
Workshop from https://www.archie.ai/ HQ in San Francisco. In this workshop we're building a Bot using Natural Language Processing. The tutorial will cover the basics of NLP, how NLP plays a major role in giving proper responses and, by the end of the tutorial you can build one yourself. Team Blog: https://medium.com/archieai Archie Bot on Amazon Alexa: https://www.amazon.com/Archie-AI-Archie-Voice/dp/B07525T7R9 Archie Bot on Google Assistant: https://assistant.google.com/services/a/id/11706af6f3eb919f/ Archie Bot on chrome extension: https://chrome.google.com/webstore/detail/archieai-google-analytics/dehldelopfcidgmfdbgaljofaemkkjcg?hl=en
Views: 2254 Archie AI
Speaker: Stephen Clark (University of Cambridge) Title: Compositional and distributional models of meaning for natural language Event: Flowin'Cat 2010 (October 2010, University of Oxford) Slides: http://www.cs.ox.ac.uk/quantum/slides/flowincat-stephenclark.pdf Abstract: There have been two main approaches to modelling the meaning of language in Computational Linguistics and Natural Language Processing. The first, the compositional approach, is based on classical ideas from Philosophy and Mathematical Logic, and implements the ideas from Formal Semantics which followed Montague's work in the 1970s. The second, more recent, approach focuses on the meanings of the words themselves. This is the distributional approach to lexical semantics and is based on the ideas of structural linguists such as Firth, and is sometimes related to the Wittgensteinian philosophy of ``meaning as use". The idea is that the meanings of words can be determined by considering the contexts in which words appear in text. In this introductory talk I will survey some recent advances in natural language parsing and lexical semantics which have led to language processing tools which can efficiently and accurately produce sophisticated linguistic representations of naturally occurring text. The talk will also serve as a tutorial for later talks in the meeting, in particular those which consider how the two approaches to meaning can be combined, to produce a unified theory of compositional distributional semantics.
Views: 2765 OxfordQuantumVideo
One of the largest elements to any data analysis, natural language processing included, is pre-processing. This is the methodology used to "clean up" and prepare your data for analysis. One of the first steps to pre-processing is to utilize stop-words. Stop words are words that you want to filter out of any analysis. These are words that carry no meaning, or carry conflicting meanings that you simply do not want to deal with. The NLTK module comes with a set of stop words for many language pre-packaged, but you can also easily append more to this list. 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: 144917 sentdex
In this Building with Watson webinar, IBM Watson developer Joshua Elliott demonstrates how Watson's Natural Language Understanding service works by analyzing text to extract meta-data from content and provides some common use cases. For more information on NLU, visit https://ibm.co/2oLMVLV
Views: 8084 IBM Watson
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: 4647 The Audiopedia
Natural Language Processing techniques allow addressing tasks like text classification and information extraction and content generation. They can give the perception of machines being able to understand humans and respond more naturally. In this session, Barbara will introduce basic concepts of natural language processing. Using Python and its machine learning libraries the attendees will go through the process of building the bag of words representation and using it for text classification. It can be then used to recognise the sentiment, category or the authorship of the document. The goal of this tutorial is to build the intuition on the simple natural language processing task. After this session, the audience will know basics of the text representation, learn how to develop the classification model and use it in real-world applications. NDC Conferences https://ndcminnesota.com https://ndcconferences.com
Views: 1947 NDC Conferences
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: 691 Data Driven NYC
Natural language processing is a popular buzzword, but have you heard of natural language generation? Marc Zionts, CEO of Automated Insights, explains NLG. Learn more about Automated Insights: https://automatedinsights.com/ #NLP #machinelearning #AI #NLG #machinelearningalgorithms ---- Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Not a member? Visit https://forbestechcouncil.com/ to see if you qualify Follow Forbes Tech Council on Twitter: https://twitter.com/ForbesTechCncl Follow Forbes Tech Council on Facebook: https://www.facebook.com/ForbesTechCouncil/
Views: 395 Forbes Councils
What is CONTROLLED NATURAL LANGUAGE? What does CONTROLLED NATURAL LANGUAGE mean? CONTROLLED NATURAL LANGUAGE meaning - CONTROLLED NATURAL LANGUAGE definition - CONTROLLED NATURAL LANGUAGE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Controlled natural languages (CNLs) are subsets of natural languages that are obtained by restricting the grammar and vocabulary in order to reduce or eliminate ambiguity and complexity. Traditionally, controlled languages fall into two major types: those that improve readability for human readers (e.g. non-native speakers), and those that enable reliable automatic semantic analysis of the language. The first type of languages (often called "simplified" or "technical" languages), for example ASD Simplified Technical English, Caterpillar Technical English, IBM's Easy English, are used in the industry to increase the quality of technical documentation, and possibly simplify the (semi-)automatic translation of the documentation. These languages restrict the writer by general rules such as "Keep sentences short", "Avoid the use of pronouns", "Only use dictionary-approved words", and "Use only the active voice". The second type of languages have a formal logical basis, i.e. they have a formal syntax and semantics, and can be mapped to an existing formal language, such as first-order logic. Thus, those languages can be used as knowledge representation languages, and writing of those languages is supported by fully automatic consistency and redundancy checks, query answering, etc.
Views: 248 The Audiopedia
Today, we are going to take a first look at the new natural language feature from api.ai in AutoVoice. At the time of this video, this feature is only available in the new AutoVoice Beta. ----------------- UPDATE ----------------- The Autovoice Natural Language feature is no longer in beta so you don't need to become a beta tester anymore to get this feature. You can download AutoVoice from here https://goo.gl/HEuI20 ----------------- SUPPORT THE CHANNEL ----------------- PATREON: https://www.patreon.com/JuanMTech BUY ME A COFFEE: https://www.buymeacoffee.com/JuanMTech AMAZON AFFILIATE LINK: http://amzn.to/2nM3bvM ----------------- VISIT MY WEBSITE ----------------- https://www.juanmtech.com ----------------- SUBSCRIBE TO THE CHANNEL ----------------- https://www.youtube.com/c/JuanMTech?sub_confirmation=1 ----------------- FOLLOW ME ON ----------------- TWITTER: https://twitter.com/juanmtech FACEBOOK: https://facebook.com/JuanMTech INSTAGRAM: https://instagram.com/juanmtech GOOGLE+: https://plus.google.com/+JuanMTech ----------------- MY GEAR ----------------- Camera: http://amzn.to/2nd3nIt Lens 1: http://amzn.to/2nFL7CO Lens 2: http://amzn.to/2o57cxW Mic 1: http://amzn.to/2qh5Iiq Mic 2: http://amzn.to/2m75JIE Tripod: http://amzn.to/2oLeoh5 ----------------- DISCLOSURE ----------------- Some of the links on this video, are affiliate links, meaning, at no additional cost to you, I will earn a commission if you click through and make a purchase. ----------------- SOUNDTRACK ----------------- Song: Zaza - Be Together [NCS Release] Music provided by NoCopyrightSounds. Video Link: https://youtu.be/gEbRqpFkTBk ----------------- INTRO MUSIC ----------------- Song: Paul Flint - Sock It To Them [NCS Release] Music provided by NoCopyrightSounds. Video Link: https://youtu.be/8-ljaTacFL0 Download: https://www.hive.co/l/2gti3 ----------------- #TaskerTutorials
Views: 17428 JuanMTech