An overview of three recent papers on applying NLP to clinical settings. — So I attended EMNLP 2020, and in addition to one of the tutorials (blog post), I also attended the Clinical NLP Workshop which had many cool papers on the newest ways of applying NLP to clinical settings. …

In the post-truth era, how can we use NLP to tackle fact-checking and fake news? — One of the tutorials at EMNLP 2020 is “Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era.” I attended this tutorial because I had done some previous research in this area and found it quite fascinating. …

A quick explanation of using curriculum learning for medical image analysis. — General Overview. In this study, I worked with a team of researchers to apply curriculum learning to improve the accuracy of a deep learning model for classifying colorectal cancer images. …

With all the recent advances in NLP, it’s refreshing to see new developments in real-world applications of the field. — A couple of years ago, I watched Google reveal their new Google Duplex system. If you’re not familiar with Duplex, it’s essentially an AI that helps complete tasks over the phone (e.g., scheduling an appointment, booking a reservation, etc.). The problem. Maybe it’s just my generation, but I’d rather text…

SemEval-2017’s Task 3 consists of five question-answering subtasks using a dataset of tens of thousands of questions and comments — One of the premier ways to evaluate NLP models, SemEval consists of several different tasks. Today we’ll be going over the 2017 version’s Task 3, including how the data for it was collected and also the five unique question-answering subtasks that it presents. The Data. As SemEval is a pretty…

A quick guide to the Stanford Sentiment Treebank (SST), one of the most well-known datasets for sentiment analysis. — Published in 2013, “Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank” presented the Stanford Sentiment Treebank (SST). SST is well-regarded as a crucial dataset because of its ability to test an NLP model’s abilities on sentiment analysis. Let’s go over this fascinating dataset.

Embeddings from Language Models (ELMo) is a state-of-the-art language modeling idea. What makes it so successful? — Published in 2018, “Deep Contextualized Word Embeddings” presented the idea of Embeddings from Language Models (ELMo), which achieved state-of-the-art performance on many popular tasks including question-answering, sentiment analysis, and named-entity extraction. ELMo has been shown to yield performance improvements of up to almost 5%. …

All the basic information you need to know about the Stanford Question Answering Dataset (SQuAD). — The Stanford Question Answering Dataset (SQuAD) is a set of question and answer pairs that present a strong challenge for NLP models. Whether you’re just interested in learning about a popular NLP dataset or planning to use it in one of your projects, here are all the basics you should…

A fun application of state-of-the-art methods in natural language processing that demonstrates how far the field has come. — Natural language processing (NLP) as a field has seen unprecedented growth (especially in the past 2 years due to the publication of BERT). And while much research is focused on tasks with huge implications (e.g., question-answering, text summarization), we should always remember that there are some fun applications of natural…