Natural Language Processing In 5 Minutes | What Is NLP And How Does It Work? | Simplilearn

00:05:29
https://www.youtube.com/watch?v=CMrHM8a3hqw

Resumen

TLDRThe video introduces Natural Language Processing (NLP), a field of artificial intelligence enabling machines to understand human languages. It covers how NLP integrates linguistics and computer science to interpret text and speech. Key techniques include segmentation, tokenization, stemming, lemmatization, and part of speech tagging. The widespread application of NLP in daily tasks is highlighted, demonstrating its significance in technology today. Viewers are encouraged to engage with a quiz and consider a postgraduate program in NLP.

Para llevar

  • 🤖 NLP allows machines to understand human languages.
  • 📈 It's crucial in automating responses and saving time.
  • 🔍 Techniques like tokenization break text into words.
  • 📝 Stemming reduces words to their base forms.
  • 📚 Learning NLP can lead to lucrative career opportunities.
  • 💡 Everyday use cases include spell check and plagiarism detection.
  • 👩‍💻 Training machines involves simple grammar techniques.
  • 💬 Part of speech tagging helps identify word categories.
  • 📊 Data analysts use NLP to understand customer behavior.
  • 🎓 Educational programs can help you become an NLP expert.

Cronología

  • 00:00:00 - 00:05:29

    The video introduces natural language processing (NLP) as a form of artificial intelligence that enables machines to understand and respond to human language in an intelligent way. It highlights the relevance of NLP in daily interactions with smart assistants and online queries, emphasizing how it mimics human linguistic behavior and saves time and manpower. The discussion extends to common applications of NLP such as autocorrect and plagiarism checkers, showcasing its widespread utility. The learning process of NLP is explained in simple terms, outlining steps like segmentation, tokenization, stemming, limitization, part of speech tagging, and named entity tagging. These steps involve breaking down text into manageable parts and categorizing words for machine understanding. The video concludes with potential career opportunities in NLP and encourages viewers to participate in a quiz. It also promotes a postgraduate program in AI and machine learning offered by Simplylearn in collaboration with IBM.

Mapa mental

Vídeo de preguntas y respuestas

  • What is NLP?

    NLP stands for Natural Language Processing, a branch of AI that allows machines to understand and derive meaning from human languages.

  • How does NLP save time and manpower?

    NLP allows machines to mimic human linguistic behavior, reducing the need for human presence in tasks like customer service.

  • What are some everyday applications of NLP?

    Common applications include autocorrect, plagiarism checkers, and virtual assistants.

  • What process does NLP training begin with?

    NLP training starts with document segmentation, breaking down texts into sentences.

  • What is tokenization in NLP?

    Tokenization is the process of breaking sentences into individual words, referred to as tokens.

  • What are stop words?

    Stop words are non-essential words in a sentence that do not add much meaning, like 'are' and 'the'.

  • What is the purpose of stemming in NLP?

    Stemming involves reducing words to their base form by removing prefixes and suffixes.

  • What can you learn in the suggested NLP program?

    The program covers tools like Keras and TensorFlow and offers hands-on experience in deep learning.

  • What is part of speech tagging?

    Part of speech tagging involves labeling words in a sentence with their grammatical categories.

  • Why is there a demand for NLP experts?

    With increasing automated language solutions, companies need skilled NLP professionals.

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    star wars fans would be familiar with
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    the golden life-sized hospitality robot
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    c-3po while star wars might be set in a
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    galaxy far far away the reality of
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    having machines talk and respond to us
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    in a human-like manner is already a
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    reality which keeps getting more and
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    more realistic with every passing day
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    the people you ask for queries on
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    websites your smart assistants even
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    calls made over the internet all of them
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    have one thing in common none of them
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    are actually human
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    now you must be thinking if they are not
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    human how do they manage to sound and
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    seem so human-like how do they respond
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    to me so intelligently and how are they
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    so articulate
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    this my friends is the magic of natural
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    language processing
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    what is nlp
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    natural language processing or nlp
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    refers to the branch of artificial
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    intelligence that gives the machines the
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    ability to read understand and derive
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    meaning from human languages
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    nlp combines the field of linguistics
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    and computer science to decipher
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    language structure and guidelines and to
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    make models which can comprehend break
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    down and separate significant details
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    from text and speech
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    every day humans interact with each
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    other through public social media
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    transferring vast quantities of freely
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    available data to each other
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    this data is extremely useful in
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    understanding human behavior and
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    customer habits
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    data analysts and machine learning
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    experts utilize this data to give
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    machines the ability to mimic human
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    linguistic behavior
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    this helps save millions in terms of
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    manpower and time as you don't need to
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    always have a person present at the
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    other end of a phone
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    nlp is also a lot more widespread than
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    you may realize you use it every day in
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    seemingly normal and insignificant
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    situations don't know how to correctly
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    spell a word
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    autocorrect has you covered
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    need to see if your article or thesis
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    will get flagged for copyright
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    violations
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    that's okay a plagiarism checker will
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    search through the web and find any
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    cases of published documents which may
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    match your work line by line
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    while nlp seems really cool yet a
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    cutting edge and complicated technology
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    concept
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    it is actually pretty easy to learn you
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    start off with a document or an article
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    to make your algorithm understand what
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    is going on in it you need to process it
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    into a form which is easily
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    comprehensible by the machine
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    this is no different than making a child
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    learn to read for the first time
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    you start off by performing segmentation
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    which is to break the entire document
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    down into its constituent sentences
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    you can do this by segmenting the
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    article along its punctuations like full
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    stops and commas for the algorithm to
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    understand these sentences we get the
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    words in a sentence and to explain them
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    individually to our algorithm so we
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    break down our sentence into its
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    constituent words and store them this is
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    called tokenizing where each word is
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    called a token
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    we can make the learning process faster
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    by getting rid of non-essential words
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    which do not add much meaning to our
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    statement and are just there to make our
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    statement sound more cohesive
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    these words such as are and the are
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    called stop words
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    now that we have the basic form of our
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    document we need to explain it to our
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    machine
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    we first start off by explaining that
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    some words like skipping skips skipped
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    are the same word with added prefixes
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    and suffixes
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    this is called stemming we also identify
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    the base words for different word tense
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    mood gender etc this is called
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    limitization stemming from the base word
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    lemma
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    now we explain the concept of nouns
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    verbs articles and other parts of speech
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    to the machine by adding these tags to
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    our words this is called part of speech
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    tagging
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    next we introduce our machine to pop
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    culture references and everyday names by
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    flagging names of movies important
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    personalities or locations etc that may
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    occur in the document
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    this is called named entity tagging
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    once we have our base words and tags we
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    use a machine learning algorithm like
  • 00:03:56
    naive bayes
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    to teach our model humans sentiment and
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    speech
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    at the end of the day most of the
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    techniques used in nlp are simple
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    grammar techniques that we have been
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    taught in school
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    here is a question for you
  • 00:04:09
    which of these nlp techniques is used to
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    obtain words from sentences
  • 00:04:13
    a stemming
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    b
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    tokenization
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    c limitization
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    d segmentation
  • 00:04:21
    give it a thought and leave your answers
  • 00:04:23
    in the comments section below three
  • 00:04:25
    lucky winners will receive amazon gift
  • 00:04:27
    vouchers with the increasing demand for
  • 00:04:29
    automated language solutions companies
  • 00:04:31
    are looking for nlp experts to join them
  • 00:04:33
    and are prepared to offer highly
  • 00:04:35
    lucrative salaries as well
  • 00:04:37
    if you want to learn more about nlp you
  • 00:04:39
    can check out simplylearn's postgraduate
  • 00:04:41
    program in ai and machine learning in
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    collaboration with ibm in this program
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    you will learn about frameworks like
  • 00:04:47
    keras and tensorflow and get hands-on
  • 00:04:50
    experience in deep learning to become a
  • 00:04:52
    truly experienced ai engineer that
  • 00:04:55
    brings us to the end of this video on
  • 00:04:57
    nlp
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    we hope you enjoyed this video if you
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    watching and stay tuned for more from
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Etiquetas
  • Natural Language Processing
  • NLP
  • Artificial Intelligence
  • Machine Learning
  • Tokenization
  • Stemming
  • Plagiarism Checker
  • Sentiment Analysis
  • Computer Science
  • Linguistics