Category: NLP

Articles About Natural Language Processing

Advanced, NLP, Python, Social Media, Technique, Text, Unstructured Data, Unsupervised Leave a comment

Summarize Twitter Live data using Pretrained NLP models

Introduction Twitter users spend an average of 4 minutes on social media Twitter. On an average of 1 minute, they read the same stuff. It shows that users spend around 25% of their time reading the same stuff. Also, most of the tweets will not appear on your dashboard. You may get to know the trending topics, but you miss not trending topics. In trending topics, you might only read the top 5 tweets and their comments. So, what are […]

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Advanced, Deep Learning, NLP, Project, Python, Text, Unstructured Data, Unsupervised Leave a comment

Tired of Reading Long Articles? Text Summarization will make your task easier!

This article was published as a part of the Data Science Blogathon. Introduction Millions of web pages and websites exist on the Internet today. Going through a vast amount of content becomes very difficult to extract information on a certain topic. Google will filter the search results and give you the top ten search results, but often you are unable to find the right content that you need. There is a lot of redundant and overlapping data in the articles […]

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Beginner, Data Cleaning, Libraries, NLP, Python, Text Leave a comment

A Quick Guide to Text Cleaning Using the nltk Library

This article was published as a part of the Data Science Blogathon. Introduction NLTK is a string processing library that takes strings as input. The output is in the form of either a string or lists of strings. This library provides a lot of algorithms that helps majorly in the learning purpose. One can compare among different variants of outputs. There are other libraries as well like spaCy, CoreNLP, PyNLPI, Polyglot. NLTK and spaCy are most widely used. Spacy works […]

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Intermediate, NLP, Project, Python, Text Leave a comment

Words that matter! A Simple Guide to Keyword Extraction in Python

This article was published as a part of the Data Science Blogathon. Introduction Unstructured data contains a plethora of information. It is like energy when harnessed, will create high value for its stakeholders. A lot of work is already being done in this area by various companies. There is no doubt that the unstructured data is noisy and significant work has to be done to clean, analyze, and make them meaningful to use. This article talks about an area which […]

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COMET, NLP

Issue #109 – COMET- the Crosslingual Optimised Metric for Evaluation of Translation

26 Nov20 Issue #109 – COMET- the Crosslingual Optimised Metric for Evaluation of Translation Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction In today’s blog post we take a look at COMET, one of the frontrunners this year at the annual WMT metrics competition (when looking across all language pairs) (Mathur et al., 2020). Historically, Machine Translation (MT) quality is evaluated by comparing the MT output with a human translated reference, and using metrics which increasingly are becoming […]

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Advanced, Computer Vision, Deep Learning, Image, Image Analysis, NLP, Python, Technique, Unstructured Data Leave a comment

A Hands-on Tutorial to Learn Attention Mechanism For Image Caption Generation in Python

Overview Understand the attention mechanism for image caption generation Implement attention mechanism to generate caption in python   Introduction The attention mechanism is a complex cognitive ability that human beings possess. When people receive information, they can consciously ignore some of the main information while ignoring other secondary information. This ability of self-selection is called attention. The attention mechanism allows the neural network to have the ability to focus on its subset of inputs to select specific features.  In recent […]

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NLP, Terminology Constraints

Issue #108 – Terminology-Constrained Neural Machine Translation at SAP

19 Nov20 Issue #108 – Terminology-Constrained Neural Machine Translation at SAP Author: Dr. Jingyi Han, Machine Translation Scientist @ Iconic Introduction Nowadays, Neural Machine Translation (NMT) has achieved impressive progress for most of the common language pairs, when enough training materials are available. However, the output is still not as promising for many cases of specific domains that are handled daily by the translation industry. How to enable NMT to properly translate terminology has always been a challenge in production […]

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Advanced, Classification, NLP, Python, Technique, Text, Unstructured Data Leave a comment

Fine-Grained Sentiment Analysis of Smartphone Review

How to conduct fine-grained sentiment analysis: Approaches and Tools Data collection and preparation. For data collection, we scraped the top 100 smartphone reviews from Amazon using python, selenium, and beautifulsoup library. If you don’t know how to use python and beautifulsoup and request a library for web-scraping here is a quick tutorial. Selenium Python bindings provide a simple API to write functional/acceptance tests using Selenium WebDriver. Let’s begin coding    

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NLP

Hugging Face – 🤗Hugging Face Newsletter Issue #1 – Aug 20th 2020

News 🤗Welcome to the Hugging Face Newsletter! 🤗 Every few weeks, we’ll be updating you on the latest happenings at Hugging Face. Make sure to subscribe and share with all NLP lovers to get the latest updates on releases, readings, research, and more! Have an idea for the newsletter? Email newsletter@huggingface.co 🚀 Model Hub Highlights 🚀 Open-Source Machine TranslationDid you know that you can translate between many languages with open-source 🤗 Transformers and great models    

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NLP

Hugging Face – 🤗Hugging Face Newsletter Issue #2 – Sep 11th 2020

News Transformers gets a new release: v3.1.0 This new version is the first PyPI release to feature: The PEGASUS models, the current State-of-the-Art in summarization DPR, for open-domain Q&A research mBART, a multilingual encoder-decoder model trained using the BART objective Alongside the three new models, we are also releasing a long-awaited feature: “named outputs”. By passing return_dict=True, model outputs can now be accessed as named values as well as by    

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