The Art of Building a Chatbot
With the development of deep learning and NLP chatbots become more and more popular. The hype for chatbots is already high and it will be increasing for the next several years. “By 2020, over 50% of medium to large enterprises will have deployed product chatbots” — Van Baker, research vice president... Read more
NLP: Extracting the Main Topics from your Dataset Using LDA in Minutes
I recently started learning about Latent Dirichlet Allocation (LDA) for topic modelling and was amazed at how powerful it can be and at the same time quick to run. Topic Modelling is the task of using unsupervised learning to extract the main topics (represented as a set of words)... Read more
NLP — Building a Question Answering model
I recently completed a course on NLP through Deep Learning (CS224N) at Stanford and loved the experience. Learnt a whole bunch of new things. For my final project I worked on a question answering model built on Stanford Question Answering Dataset (SQuAD). In this blog, I want to cover the... Read more
Using Object Detection for a Smarter Retail Checkout Experience
I have been playing around with the Tensorflow Object Detection API and have been amazed by how powerful these models are. I want to share the performance of the API for some practical use cases. The first use case is a smarter retail checkout experience. This is a hot field right... Read more
A Research-Oriented Look at the Evolution of Word Embedding: Part II
This article is the second article in a two-part series about the evolution of word embedding as told through the context of five research papers. It picks up in midst of the 1990s. To view the first article, click here. A Shift Towards Automatic Feature Generation: Latent Dirichlet Allocation... Read more
A Research-Oriented Look at the Evolution of Word Embedding: Part I
Introduction “You shall know a word by the company it keeps,” insisted John R. Firth, a British linguist who performed pioneering work on collocational theories of semantics. What Firth meant by his 1957 quote was that interrogating the context in which a word is found offers clues to the... Read more
NLP’s ImageNet Moment Has Arrived
This post originally appeared at TheGradient and was edited by Andrey Kurenkov, Eric Wang, and Aditya Ganesh. Big changes are underway in the world of Natural Language Processing (NLP). The long reign of word vectors as NLP’s core representation technique has seen an exciting new line of challengers emerge: ELMo, ULMFiT, and the OpenAI transformer. These works made headlines by... Read more
An Attempt to Chart the History of NLP in 5 Papers: Part II
This article is the second article in a two-part series about the history of NLP as told through the context of five research papers. It picks up in midst of the 1970s. To view the first article, click here. Corpus resource development The relation-driven academic era that spilled into... Read more
Using Machine Learning to Read Sherlock Holmes
A while ago I posted about how to use machine learning to understand brand semantics by mining Twitter data — not just to count mentions, but to map the similitudes and differences in how people think about them. But individual tweets are brief snapshots, just a few words written and posted... Read more
3 NLP Trends on the Rise in 2018
With advances in computational power and the integration of artificial intelligence, the natural language processing domain has evolved into a whirlwind of innovation. In fact, experts expect the NLP market to swell to an impressive $22.3 billion by 2025. In the meantime, here’s a look at three NLP trends... Read more
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