Machine Translation Weekly 55: Social Polarization Seen through Word Embeddings

This week, I am going to have a closer look at a paper that creatively uses methods for bilingual word embeddings for social media analysis. The paper’s preprint was uploaded last week on arXiv. The title is “We Don’t Speak the Same Language: Interpreting Polarization through Machine Translation,” and most of the authors CMU in Pittsburgh. The paper’s central assumption is that the polarization of different opinion groups, especially in the USA, went so far that some words have totally […]

October 16, 2020 Python

Generating Synthetic Data with Numpy and Scikit-Learn

Introduction In this tutorial, we’ll discuss the details of generating different synthetic datasets using Numpy and Scikit-learn libraries. We’ll see how different samples can be generated from various distributions with known parameters. We’ll also discuss generating datasets for different purposes, such as regression, classification, and clustering. At the end we’ll see how we can generate a dataset that mimics the distribution of an existing dataset. The Need for Synthetic Data In data science, synthetic data plays a very important role. […]

October 16, 2020 Legal-BERT, NLP

Issue #103 – LEGAL-BERT: The Muppets straight out of Law School

16 Oct20 Issue #103 – LEGAL-BERT: The Muppets straight out of Law School Author: Akshai Ramesh, Machine Translation Scientist @ Iconic Introduction BERT (Bidirectional Encoder Representations from Transformers) is a large-scale pre-trained autoencoding language model that has made a substantial contribution to natural language processing (NLP) and has been studied as a potentially promising way to further improve neural machine translation (NMT). “Given that BERT is based on a similar approach to neural MT in Transformers, there’s considerable interest and […]

October 15, 2020 Machine Learning

Nearest Shrunken Centroids With Python

Nearest Centroids is a linear classification machine learning algorithm. It involves predicting a class label for new examples based on which class-based centroid the example is closest to from the training dataset. The Nearest Shrunken Centroids algorithm is an extension that involves shifting class-based centroids toward the centroid of the entire training dataset and removing those input variables that are less useful at discriminating the classes. As such, the Nearest Shrunken Centroids algorithm performs an automatic form of feature selection, […]

October 14, 2020 Beginner, Business Analytics, Infographic, Infographics, NLP, Python, Technique, Text, Unstructured Data Leave a comment

Quick Guide: Steps To Perform Text Data Cleaning in Python

Introduction Twitter has become an inevitable channel for brand management. It has compelled brands to become more responsive to their customers. On the other hand, the damage it would cause can’t be undone. The 140 character tweets has now become a powerful tool for customers / users to directly convey messages to brands. For companies, these tweets carry a lot of information like sentiment, engagement, reviews and features of its products and what not. However, mining these tweets isn’t easy. Why? Because, before you mine this data, you need […]

October 14, 2020 Intermediate, Machine Learning, NLP, Programming, Python, Technique, Text, Unstructured Data Leave a comment

Introduction to Structuring Customer complaints explained with examples

Introduction In past, if you were not particularly happy with a service or a product, you would go to the service provider or the shop and lodge a complaint. With services-businesses going online and due to enormous scale, lodging complaints in-person may not be always possible. Electronic ways such as emails, social media and particularly websites like www.consumercomplaints.in focusing on such issues, are widely used platforms to vent out the anger as well as publicizing the issue in expectancy of […]

October 14, 2020 Artificial intelligence Leave a comment

Novel object captioning surpasses human performance on benchmarks

Consider for a moment what it takes to visually identify and describe something to another person. Now imagine that the other person can’t see the object or image, so every detail matters. How do you decide what information is important and what’s not? You’ll need to know exactly what everything is, where it is, what it’s doing in relation to other objects, and note other attributes like color or position of objects in the foreground or background. This exercise shows […]

October 13, 2020 Python

Simple NLP in Python With TextBlob: Tokenization

Introduction The amount of textual data on the Internet has significantly increased in the past decades. There’s no doubt that the processing of this amount of information must be automated, and the TextBlob package is one of the fairly simple ways to perform NLP – Natural Language Processing. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, tokenization, sentiment analysis, classification, translation, and more. No special technical prerequisites […]

October 13, 2020 Python

Add Legend to Figure in Matplotlib

Introduction Matplotlib is one of the most widely used data visualization libraries in Python. Typically, when visualizing more than one variable, you’ll want to add a legend to the plot, explaining what each variable represents. In this article, we’ll take a look at how to add a legend to a Matplotlib plot. Creating a Plot Let’s first create a simple plot with two variables: import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots() x = np.arange(0, 10, […]

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