Zara  Bryant

Zara Bryant


Introduction to Natural Language Processing (NLP)

So, What is NLP?

NLP is an interdisciplinary field concerned with the interactions between computers and human natural languages (e.g: English) — speech or text. NLP-powered softwares help us in our daily lives in various ways, for example:

  • Personal assistants: Siri, Cortana, and Google Assistant.
  • Auto-complete: In search engines (e.g: Google, Bing).
  • Spell checking: Almost everywhere, in your browser, your IDE (e.g: Visual Studio), desktop apps (e.g: Microsoft Word).
  • Machine Translation: Google Translate.

Okay, now we get it, NLP plays a major role in our daily computer interactions, let’s see more business-related NLP use-cases:

  • If you have a bank or a restaurant with a huge load of customers orders and complaints, handling this in a manual way will be tiresome and repetitive, also not very efficient in terms of time and labor, so you can build a chat-bot for your business, which will automate such process and reduce human interaction.
  • Apple will soon launch the new iPhone 11, and they will be interested to know what users are thinking of the new iPhone, so they can monitor social media channels (e.g: Twitter), and extract iPhone 11 related tweets, reviews, and opinions, then use sentiment analysis models to predict whether users’ reviews are positive, negative, or neutral.


NLP is divided into two fields: Linguistics and Computer Science.

The Linguistics side is concerned with language, it’s formation, syntax, meaning, different kind of phrases (noun or verb) and whatnot.

The Computer Science side is concerned with applying linguistic knowledge, by transforming it into computer programs with the help of sub-fields such as **Artificial Intelligence **(Machine Learning & Deep Learning).

Let’s Talk Science!

Scientific advancements in NLP can be divided into 3 categories(Rule-based systems, Classical Machine Learning models and Deep Learning models).

  • Rule-based systems rely heavily on crafting domain-specific rules (e.g: regular expressions), can be used to solve simple problems such as extracting structured data (e.g: emails) from unstructured data (e.g: web-pages), but due to the complexity of human natural languages, rule-based systems fail to build models that can really reason about language.
  • Classical Machine Learning approaches can be used to solve harder problems which rule-based systems can’t solve very well (e.g: Spam Detection), it rely on a more general approach to understanding language, using hand-crafted features (e.g: sentence length, part of speech tags, occurrence of specific words) then providing those features to a statistical machine learning model (e.g: Naive Bayes), which learns different patterns in the training set and then be able to reason about unseen data (inference).
  • Deep Learning models are the hottest part of NLP research and applications now, they generalize even better than the classical machine learning approaches as they don’t need hand-crafted features because they work as feature extractors in an automatic way, which helped a lot in building end-to-end models (little human-interaction). Aside from the feature engineering part, deep learning algorithms learning capabilities are more powerful than the shallow/classical ML ones, which paved its way to achieving the highest scores on different hard NLP tasks (e.g: Machine Translation).

How Does Computer Understand Text?

We do know that computers only understand numbers, not characters, words, or sentences, so an intermediate step is needed before building NLP models, which is text representation**. **I will focus on word-level representations, as it’s the most widely used and intuitive ones to start with, other representations can be used such as bit, character, sub-word, and sentence level representations).

  • In traditional NLP era (before deep learning) text representation was built on a basic idea, which is one-hot encodings, where a sentence is represented as a matrix of shape (NxN) where N is the number of unique tokens in the sentence, for example in the above picture, each word is represented as a sparse vectors (mostly zeroes) except of one cell (could be one, or the number of occurrences of the word in the sentence). This approach has two major drawbacks, the first one is the huge memory capacity issues (hugely sparse representation), the second one is its lack of meaning representation, such that it can’t derive similarities between words (e.g: school and book).

  • In 2013, researchers from Google (lead by Thomas Mikolov), has invented a new model for text representation (which was revolutionary in NLP), called word2vec, a shallow deep learning model which is able to represent words in dense vectors, and capture semantic meaning between related terms (e.g: Paris and France, Madrid and Spain). Further research has built on top of word2vec, such as GloVe, fastText.

Tasks & Research

Let’s take a look at some NLP tasks and categorize them based on the research progress for each task.

1) Mostly solved:

  • Spam Detection (e.g: Gmail).
  • Part of Speech (POS) tagging: Given a sentence, determine POS tags for each word (e.g: NOUN, VERB, ADV, ADJ).
  • Named Entity Recognition (NER): Given a sentence, determine named entities (_e.g: _person names, locations, organizations).

2) Making good progress:

  • Sentiment Analysis: Given a sentence, determine it’s polarity (e.g: positive, negative, neutral), or emotions (e.g: happy, sad, surprised, angry)
  • Co-reference Resolution: Given a sentence, determine which words (“mentions”) refer to the same objects (“entities”). for example (Manning is a great NLP professor, he worked in academia for over 25 years).
  • Word Sense Disambiguation (WSD): Many words have more than one meaning, we have to select the meaning which makes the most sense in context (e.g: I went to the bank to get some money), here bank means a financial institution, not the land beside a river.
  • Machine Translation (e.g: Google Translate)

3) Still a bit hard:

  • Dialogue agents and chat-bots, especially open domain ones.
  • Question Answering.
  • Summarization.
  • NLP for low resource languages.

NLP Online Demos

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Now we have covered what is NLP, the science behind it and how to study it, let’s get to the practical part, here’s a list of the top widely used open source libraries to use in your next project.

So that was an end-to-end introduction to Natural Language Processing, hope that helps, and if you have any suggestions, please leave them in the responses. Cheers!

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What is GEEK

Buddha Community

Introduction to Natural Language Processing (NLP)

8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.

Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.

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Sival Alethea

Sival Alethea


Natural Language Processing (NLP) Tutorial with Python & NLTK

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.

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Ray  Patel

Ray Patel


Introduction to Natural Language Processing

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).

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.

Deep Learning: Dive into the World of Machine Learning!

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!

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Paula  Hall

Paula Hall


Structured natural language processing with Pandas and spaCy

Accelerate analysis by bringing structure to unstructured data

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.

Introduction to Spacy

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.

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Cayla  Erdman

Cayla Erdman


Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

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