Articles About Natural Language Processing

AAAI 2019 Highlights: Dialogue, reproducibility, and more

This post discusses highlights of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19). I attended AAAI 2019 in Honolulu, Hawaii last week. Overall, I was particularly surprised by the interest in natural language processing at the conference. There were 15 sessions on NLP (most standing-room only) with ≈10 papers each (oral and spotlight presentations), so around 150 NLP papers (out of 1,150 accepted papers overall). I also really enjoyed the diversity of invited speakers who discussed topics from AI for […]

Read more

Build a word cloud using text mining tools of R

 This is how a word cloud of our entire website looks like! A word cloud is a graphical representation of frequently used words in a collection of text files. The height of each word in this picture is an indication of frequency of occurrence of the word in the entire text. By the end of this article, you will be able to make a word cloud using R on any given set of text files. Such diagrams are very useful when doing […]

Read more

6 Practices to enhance the performance of a Text Classification Model

Introduction A few months back, I was working on creating a sentiment classifier for Twitter data. After trying the common approaches, I was still struggling to get good accuracy on the results. Text classification problems and algorithms have been around for a while now. They are widely used for Email Spam Filtering by the likes of Google and Yahoo, for conducting sentiment analysis of twitter data and automatic news categorization in google alerts. However, while dealing with enormous amount of text […]

Read more

Extracting information from reports using Regular Expressions Library in Python

Introduction Many times it is necessary to extract key information from reports, articles, papers, etc. For example names of companies – prices from financial reports, names of judges – jurisdiction from court judgments, account numbers from customer complaints, etc. These extractions are part of Text Mining and are essential in converting unstructured data to a structured form which are later used for applying analytics/machine learning. Such entity extraction uses approaches like ‘lookup’, ‘rules’ and ‘statistical/machine learning’. In ‘lookup’ based approaches, […]

Read more

Essentials of Deep Learning : Introduction to Long Short Term Memory

Introduction Sequence prediction problems have been around for a long time. They are considered as one of the hardest problems to solve in the data science industry. These include a wide range of problems; from predicting sales to finding patterns in stock markets’ data, from understanding movie plots to recognizing your way of speech, from language translations to predicting your next word on your iPhone’s keyboard. With the recent breakthroughs that have been happening in data science, it is found […]

Read more

Replicating Human Memory Structures in Neural Networks to Create Precise NLU algorithms

Introduction Machine learning and Artificial Intelligence developments are happening at breakneck speed! At such pace, you need to understand the developments at multiple levels – you obviously need to understand the underlying tools and techniques, but you also need to develop an intuitive understanding of what is happening. By the end of this article, you will develop an intuitive understanding of RNNs, especially LSTM & GRU. Ready? Let’s go!   Table of Contents Simple exercise – Tweet classification How does […]

Read more

An NLP Approach to Mining Online Reviews using Topic Modeling (with Python codes)

Introduction E-commerce has revolutionized the way we shop. That phone you’ve been saving up to buy for months? It’s just a search and a few clicks away. Items are delivered within a matter of days (sometimes even the next day!). For online retailers, there are no constraints related to inventory management or space management They can sell as many different products as they want. Brick and mortar stores can keep only a limited number of products due to the finite space […]

Read more

Introduction to StanfordNLP: An Incredible State-of-the-Art NLP Library for 53 Languages (with Python code)

Introduction A common challenge I came across while learning Natural Language Processing (NLP) – can we build models for non-English languages? The answer has been no for quite a long time. Each language has its own grammatical patterns and linguistic nuances. And there just aren’t many datasets available in other languages. That’s where Stanford’s latest NLP library steps in – StanfordNLP. I could barely contain my excitement when I read the news last week. The authors claimed StanfordNLP could support more […]

Read more

DataHack Radio #23: Ines Montani and Matthew Honnibal – The Brains behind spaCy

https://soundcloud.com/datahack-radio/ines-montani-matthew-honnibal-the-brains-behind-spacy Introduction What would you do if you had the chance to pick the brains behind one of the most popular Natural Language Processing (NLP) libraries of our era? A library that has helped usher in the current boom in NLP applications and nurtured tons of NLP scientists? Well – you invite the creators on our popular DataHack Radio podcast and let them do the talking! We are delighted to welcome Ines Montani and Matt Honnibal, the developers of spaCy […]

Read more

Building a Recommendation System using Word2vec: A Unique Tutorial with Case Study in Python

Overview Recommendation engines are ubiquitous nowadays and data scientists are expected to know how to build one Word2vec is an ultra-popular word embeddings used for performing a variety of NLP tasks We will use word2vec to build our own recommendation system. Curious how NLP and recommendation engines combine? Let’s find out!   Introduction Be honest – how many times have you used the ‘Recommended for you’ section on Amazon? Ever since I found out a few years back that machine […]

Read more
1 54 55 56 57 58 71