Rohit Prasad, head scientist for Alexa, recently published an article questioning whether the Turing Test is still a relevant benchmark for…

 Natural Language Processing Defeats User Trepidation About Augmented Analytics

Your business users probably fight you on improving data literacy and on implementing digital transformation. Many business users have a fear of analytics and envision having to become a business analyst or a data scientist in order to…

In this blog post, I shall discuss on a few NLP techniques with Bangla language, demonstrate how to train a word2vec model with Bangla wiki corpus with tensorflow, and explore how to visualize the semantic similarity between words using t-SNE. Once done, the article will focus on how to train a character / word LSTM on selected Tagore’s…

Summary: Since BERT NLP models were first introduced by Google in 2018 they have become the go-to choice.  New evidence however shows that LSTM models may widely outperform BERT meaning you may need to evaluate both approaches for your NLP project.

Over the last year or two, if you needed to bring in an NLP…

Milvus Project: github.com/milvus-io/milvus

The question answering system is commonly used in the field of natural language processing. It is used to answer questions in the form of natural language and has a wide range of applications. Typical applications include intelligent voice interaction, online customer service,…

Summary:  Objectively identifying hateful or abusive speech on social media platforms would allow those platforms to better control it.  However to be objective and without bias that identification would have to be independent of the author especially where elected officials are involved.

What could be more…

Chatbots are virtual agents capable of emulating the conversation of a human. Chatbots are becoming very popular in providing online services or queries. Recently, chatbots have been gaining lots of limelight due to the development of Natural Language Processing (NLP) capabilities. Today, a chatbot can respond in a similar way a human agent would. Being devoid of emotions, a chatbot can be expected to offer the same quality of service throughout the day.

Despite the several…

ELAINE Symbolic AI offers community tool to cure “TL;DR” Syndrome

The idea of applying Natural Language tool to advance human intelligence is not new. Examples can be found among popular search engines and chat-bots. These applications generally require Machine Learning ahead of lengthy preparations with “human in the loop” training datasets. These pre-requisites are cost intensive in terms of time, labor, infrastructure and skill.

A few years…

By Faruqui Ismail and Nooka Raju Garimella

Reporters with various forms of "fake news" from an 1894 illustration by Frederick Burr Opper

We’ve always pictured the rise of artificial…

Summary:  Contextually intelligent, NLP-based interactive assistants are one of the next big things for AI/ML.  The tech is already here from recommendation engines.  The need to be more efficient and to become AI-augmented in our decision making is now.  Getting the contextual awareness is the hard part.

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Chatbots are a hot topic. Conversational commerce and Artificial Intelligence are at the peak; if you haven’t already experimented with chatbots for your customer services, now’s the time. But how do you create a chatbot yourself? Let’s review the options from the simplest to the most complicated.

Menu-based chatbots

The simplest…

Summary:  99% of our application of NLP has to do with chatbots or translation.  This is a very interesting story about expanding the bounds of NLP and feature creation to predict bestselling novels.  The authors created over 20,000 NLP features, about 2,700 of which proved to be predictive with a 90% accuracy rate in predicting NYT bestsellers.

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Neural networks have made significant leaps in the image and natural language processing (NLP) recently. They’ve not only learned to recognize, localize and segment images; they’re now able to effectively translate natural language and answer complex questions. One of the precursors to such massive progress was the introduction of Seq2Seq and…

Nowadays, lot's of discussion is happening around the question of what artificial intelligence can and can't do. Even though artificial intelligence has a controversial status, this technology already has some real-life business applications and delivers proven results.

Building a meaningful interaction with…

Introduction to topic model:

In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. 

In topic modeling, a topic is defined by a cluster of words with each word in the cluster having a probability of occurrence for the given topic, …

Summary: Recurrent Neural Nets (RNNs) are at the core of the most common AI applications in use today but we are rapidly recognizing broad time series problem types where they don’t fit well.  Several alternatives are already in use and one that’s just been introduced, ODE net is a radical departure from our way of thinking about the solution.

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How machines process and understand human language

Everything we express (either verbally or in written) carries huge amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and value extracted from it. In theory, we can understand and even predict human behaviour using that information.…

Measuring the similarity between texts is a common task in many applications. It is useful in classic NLP fields like search, as well as in such far from NLP areas as medicine and genetics. There are many different approaches of how to compare two texts (strings of characters). Each has its own advantages and disadvantages and is good only…

What is NLP?

Natural Language Processing (NLP) can be simply defined as teaching an algorithm to read and analyze human (natural) languages just like the human brain does, but a lot faster than a human could, more accurately and on very large amounts of data.

It is a great skill to have if you are an aspiring data scientist or data analyst because has…