It’s all about T-E-X-T

May 19, 2020

Last week, we held the first AI-ML Virtual forum of IsraelClouds and Ai-Blog, collaborating with IBM. We can proudly write that we really hit the bar with this event, as we reached an amazing 88 (!) Active participants that were connected online, fascinated by the professional lectures and eager to ask and learn more.

During the Forum, we heard two very interesting lectures. The first one was about simplifying text analysis with Watson's services and the second was about Word Embedding: Theory and practice.

In the first session, Tal Neeman, Developer Advocate, IBM, conducted a live Hands-on tutorial. Tal explained about the basics of Watson services that can provide the participants tools to analyze text in few lines of code. In addition, Tal spoke about 3 services that can help you get the most from your text:

1) Watson Natural Language Understanding - Cloud native product that uses deep learning to extract metadata from text such as entities, keywords, categories, sentiment, emotion, relations, and syntax.

2) Watson Discovery - Enterprise search and AI search technology that breaks open data silos and retrieves specific answers to your questions while analyzing trends and relationships buried in enterprise data. Watson Discovery applies the latest breakthroughs in machine learning, including natural language processing capabilities, and is easily trained on the language of your domain. Unlike competitors, Watson Discovery can be deployed on any cloud or on-premises environment.

3) Watson Knowledge Studio can help you teach Watson the language of your domain.

* As part of the Hands-on session, please note that you should open a free account on IBM Cloud via


In the second Session, Tal Eden, Chief data Scientist, and former Chief Data Scientist, the Sheba Innovation Center, reviewed few methods for mathematical representation of words and texts, while he focused on a specific method of word embedding, Word2Vec. Also, Tal explained about the algorithmic and mathematical foundation behind Word2Vec, and went over a simple implementation example with Gensim, a python package for topic modeling.


You can further expand your knowledge and explore the topics above, by watching the 2 professional session here:


Also, if you really want to become a Pro, you can use a free month of IBM’s Data Science Programs on Coursera:


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