Attention is all you need. That is the name of the 2017 paper that introduced attention as an independent learning model — the herald of our now transformer dominant world in natural language processing (NLP).
Transformers are the new cutting-edge in NLP, and they may seem somewhat abstract — but when we look at the past decade of developments in NLP they begin to make sense.
We will cover these developments, and look at how they have led to the Transformers being used today. This article makes no assumptions in you already understanding these concepts — we will build an intuitive understanding without getting overly technical.
We will cover:
#python #deep-learning #machine-learning #data-science #natural-language-processing #tensorflow
We’re officially a part of a digitally dominated world where our lives revolve around technology and its innovations. Each second the world produces an incomprehensible amount of data, a majority of which is unstructured. And ever since Big Data and Data Science have started gaining traction both in the IT and business domains, it has become crucial to making sense of this vast trove of raw, unstructured data to foster data-driven decisions and innovations. But how exactly are we able to give coherence to the unstructured data?
The answer is simple – through Natural Language Processing (NLP).
In simple terms, NLP refers to the ability of computers to understand human speech or text as it is spoken or written. In a more comprehensive way, natural language processing can be defined as a branch of Artificial Intelligence that enables computers to grasp, understand, interpret, and also manipulate the ways in which computers interact with humans and human languages. It draws inspiration both from computational linguistics and computer science to bridge the gap that exists between human language and a computer’s understanding.
The concept of natural language processing isn’t new – nearly seventy years ago, computer programmers made use of ‘punch cards’ to communicate with the computers. Now, however, we have smart personal assistants like Siri and Alexa with whom we can easily communicate in human terms. For instance, if you ask Siri, “Hey, Siri, play me the song Careless Whisper”, Siri will be quick to respond to you with an “Okay” or “Sure” and play the song for you! How cool is that?
Nope, it is not magic! It is solely possible because of NLP powered by AI, ML, and Deep Learning technologies. Let’s break it down for you – as you speak into your device, it becomes activated. Once activated, it executes a specific action to process your speech and understand it. Then, very cleverly, it responds to you with a well-articulated reply in a human-like voice. And the most impressive thing is that all of this is done in less than five seconds!
#artificial intelligence #big data #data sciences #machine learning #natural language processing #introduction to natural language processing
Working with natural language data can often be challenging due to its lack of structure. Most data scientists, analysts and product managers are familiar with structured tables, consisting of rows and columns, but less familiar with unstructured documents, consisting of sentences and words. For this reason, knowing how to approach a natural language dataset can be quite challenging. In this post I want to demonstrate how you can use the awesome Python packages, spaCy and Pandas, to structure natural language and extract interesting insights quickly.
spaCy is a very popular Python package for advanced NLP — I have a beginner friendly introduction to NLP with SpaCy here. spaCy is the perfect toolkit for applied data scientists when working on NLP projects. The api is very intuitive, the package is blazing fast and it is very well documented. It’s probably fair to say that it is the best general purpose package for NLP available. Before diving into structuring NLP data, it is useful to get familiar with the basics of the spaCy library and api.
After installing the package, you can load a model (in this case I am loading the simple Engilsh model, which is optimized for efficiency rather than accuracy) — i.e. the underlying neural network has fewer parameters.
import spacy nlp = spacy.load("en_core_web_sm")
We instantiate this model as nlp by convention. Throughout this post I’ll work with this dataset of famous motivational quotes. Let’s apply the nlp model to a single quote from the data and store it in a variable.
#analytics #nlp #machine-learning #data-science #structured natural language processing with pandas and spacy #natural language processing
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.
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=X2vAabgKiuM&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=16
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#natural language processing #nlp #python #python & nltk #nltk #natural language processing (nlp) tutorial with python & nltk
Introduction to Natural Language Processing (NLP)
Natural Language Processing (NLP) a subset technique of Artificial Intelligence which is used to narrow the communication gap between the Computer and Human. It is originated from the idea of Machine Translation (MT) which came to existence during the second world war.
The primary idea was to convert one human language to another human language, for example, turning the Russian language to English language using the brain of the Computers but after that, the thought of conversion of human language to computer language and vice-versa emerged, so that communication with the machine became easy.
#data science #evolution #natural language
Initial setup costs remain a barrier to market growth, as well as a lack of skilled professionals to implement NLP.
The natural language processing market, which includes machine translation, information extraction, summarization, text classification and sentiment analysis, is expected to reach a $41 billion valuation by 2025.
An increase in demand for analyzing conversations and social media, alongside other customer experience enhancements, are considered key drivers for NLP, according to Adroit Market Research.
#artificial intelligence technologies #trending now #adroit market research #artificial intelligence #natural language #natural language processing