Vectorization

why we need to convert text to a vector? Machines don’t understand data in text form. we have to convert text to vectors so that mathematical operations can be performed on data. in NLP it is also know as word embeddings.

November 14, 2022 Natural Language Processing, Python

A brief introduction to sentiment analysis using VADER-model

Source: Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text (aaai.org) This article is about the VADER-sentiment model and how it generates the sentiment scores for social media text. I recently used this library to build a very basic model

November 14, 2022 Python

Custom Pagination in python with offset and limit

Offset Pagination

November 14, 2022 Natural Language Processing, Python

Datatang Launches New Parallel Corpora for Machine Translation

November 14, 2022 Python

Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 1

Most modern web applications are powered by a REST API under the hood. That way, developers can separate the front-end code from the back-end logic, and users can interact with the interface dynamically. In this three-part tutorial series, you’ll build a REST API with the Flask web framework. You’ll create a foundation with a basic Flask project then add endpoints and connect them to a SQLite database. You’ll test your API with Swagger UI API documentation that you’ll build along […]

November 13, 2022 Natural Language Processing, Python

Core Values and the Ideal Model of the SimSimi Chatbot

In our previous post “How an AI Chatbot Works: A Quick Guide to SimSimi’s Architecture,” we demonstrated the operating mechanism behind our chatbot SimSimi. In today’s post, we would like to introduce you to SimSimi’s core values and essential functions needed to realize those values.

November 12, 2022 Natural Language Processing, Python

Legal NLP 1.2.0 for Spark NLP has been released! ⚖️

November 11, 2022 Python

A/B Testing Using Python

A/B testing, also known as split testing, compares two different versions of something (mobile app, web page, advertisement, etc.) to determine which one performs better. A/B testing is a common statistical test, especially in marketing strategy. We can see which version is making more purchases or more clicks after split testing, and then we can make strategic decisions. Many companies use A/B testing in the new technology world, like Instagram, Facebook, Google, LinkedIn, etc.