Articles About Natural Language Processing

January 13, 2021 Advanced, Classification, Deep Learning, NLP, Python, Social Media, Structured Data, Supervised, Technique, Text Leave a comment

Emotion classification on Twitter Data Using Transformers

Introduction The world of Natural language processing is recently overtaken by the invention of Transformers. Transformers are entirely indifferent to the conventional sequence-based networks. RNNs are the initial weapon used for sequence-based tasks like text generation, text classification, etc. But with the arrival of LSTM and GRU cells, the issue with capturing long-term dependency in the text got resolved. But learning the model with LSTM cells is a hard task as we cannot make it learn parallelly.

January 12, 2021 NLP

Hugging Face – Issue 5 – Dec 21st 2020

News Hugging Face Datasets Sprint 2020 This December, we had our largest community event ever: the Hugging Face Datasets Sprint 2020. It all started as an internal project gathering about 15 employees to spend a week working together to add datasets to the Hugging Face Datasets Hub backing the 🤗 datasets library. The library provides 2 main features surrounding datasets: One-line dataloaders for many public datasets: with a simple command like

January 12, 2021 NLP

Hugging Face – Special Edition – New Plans, Private Models and AutoNLP – Dec 23rd 2020

News Ho Ho Ho! welcome to a special edition of the Hugging Face newsletter focused on new and upcoming commercial products. 👩‍🔬Introducing Supporter plans for individuals, with private models 👩‍🔬 Hugging Face is built for, and by the NLP community. We share our commitment to democratize NLP with hundreds of open source contributors, and model contributors all around

January 9, 2021 Advanced, Deep Learning, NLP, Project, Python, Technique, Text Leave a comment

Out-of-the-box NLP functionalities for your project using Transformers Library!

This article was published as a part of the Data Science Blogathon. Introduction In this tutorial, you will learn how you can integrate common Natural Language Processing (NLP) functionalities into your application with minimal effort. We will be doing this using the ‘transformers‘ library provided by Hugging Face. 1. First, Install the transformers library. # Install the library !pip install transformers 2. Next, import the necessary functions. # Necessary imports from transformers import pipeline 3. Irrespective of the task that […]

January 7, 2021 Advanced, Data Exploration, NLP, Python, Resource, Text, Unstructured Data, Unsupervised, Use Cases Leave a comment

Analysis of Brazilian E-commerce Text Review Dataset Using NLP and Google Translate

This article was published as a part of the Data Science Blogathon. Introduction Comprehending the reviews of customers is very crucial for a business to be successful. Analyzing the reviews helps to properly discern the customer different preferences, likes, dislikes, etc. These extracted insights can then be used to improve customer service and experience. In this article, we would be working on a Brazilian E-commerce reviews dataset where we would perform some exploratory data analysis (EDA) on reviews text, derive […]

January 7, 2021 Advanced, Deep Learning, NLP Leave a comment

Introduction to Automatic Speech Recognition and Natural Language Processing

This article was published as a part of the Data Science Blogathon. Introduction In this article, we will take a closer look at how speech recognition really works. Now, when we say speech recognition, we’re really talking about ASR, or automatic speech recognition. With automatic speech recognition, the goal is to simply input any continuous audio speech and output the text equivalent. We want our ASR to be speaker-independent and have high accuracy. Such a system has long been a […]

January 6, 2021 Advanced, Deep Learning, NLP Leave a comment

GPT-3 THE NEXT BIG THING! Foundation of Future?

This article was published as a part of the Data Science Blogathon. Introduction Did you ever have a thought or a wish that you just wanted to write two lines of an essay or a journal and the computer just wrote the rest for you? If yes, then GPT-3 is the answer for you. Baffled? So are the people who got their hands on the GPT-3. Every field in AI is making advancements and NLP & Deep learning are such […]

January 5, 2021 NLP

Best Practices for Data-Efficient Modeling in NLG: How to Train Production-Ready Neural Models with Less Data

December 8, 2020 By: Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White Abstract Natural language generation (NLG) is a critical component in conversational systems, owing to its role of formulating a correct and natural text response. Traditionally, NLG components have been deployed using template-based solutions. Although neural network solutions recently developed in the research community have been shown to provide several benefits, deployment of […]

December 31, 2020 Advanced, Deep Learning, NLP, Project, Python, Semi-supervised, Structured Data, Technique, Text Leave a comment

Fake news classifier on US Election News📰 | LSTM 🈚

Introduction News media has become a channel to pass on the information of what’s happening in the world to the people living. Often people perceive whatever conveyed in the news to be true. There were circumstances where even the news channels acknowledged that their news is not true as they wrote. But some news has a significant impact not only on the people or

December 27, 2020 NLP

Efficient One-Pass End-to-End Entity Linking for Questions

November 16, 2020 By: Belinda Z. Li, Sewon Min, Srinivasan Iyer, Yashar Mehdad, Wen-tau Yih Abstract We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass. Evaluated on WebQSP and GraphQuestions with extended annotations that cover multiple entities per question, ELQ outperforms the previous state of the art by a large margin of +12.7% and +19.6% F1, respectively. With a very fast inference time (1.57 […]

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