NLP

By Techal Welcome to another exciting installment of our NLU and IR series! In this episode, we dive into the fascinating world of neural IR and explore the late interaction paradigm. This paradigm offers a unique approach to achieving high effectiveness and low computational cost in information retrieval tasks. So let’s get started! The Representation … Read more

Welcome to Techal! In this article, we’ll explore the concepts of stemming and lemmatization in natural language processing (NLP). These techniques are essential in the pre-processing stage of building NLP applications and can greatly enhance your text analysis capabilities. The Need for Stemming and Lemmatization When you search for a word on Google, you may … Read more

Welcome to the first article of the Techal brand’s series on Natural Language Understanding. In this article, we will explore the high-level goals and guiding hypotheses for understanding the meaning of words and how they are represented in text data. The Power of Word Co-Occurrence We begin by examining the concept of distributed word representations … Read more

Welcome back, everyone! Today, we’re going to delve into the fascinating world of supervised sentiment analysis with a focus on the Stanford Sentiment Treebank. This particular dataset is a treasure trove of information when it comes to understanding the sentiment behind sentences. The Basics The Stanford Sentiment Treebank is a sentence-level corpus consisting of approximately … Read more

Welcome to our exploration of recurrent neural network (RNN) classifiers. In this article, we’ll delve into the world of deep learning for sentiment analysis and examine the core structure and capabilities of RNN classifiers. So, strap in and get ready to expand your knowledge of this exciting technology! The Basics of RNN Classifiers RNN classifiers … Read more

Welcome to Techal’s beginner’s guide to Natural Language Processing (NLP) tasks! In this article, we will explore various types of NLP tasks and their real-life applications. Whether you are new to NLP or looking to expand your knowledge, we’ve got you covered. So let’s dive in! Text Classification: Categorizing Complaints One classic NLP task is … Read more

Welcome back, technology enthusiasts! In this article, we will explore an overview of the homework and bakeoff associated with the CS224U Natural Language Understanding class by Stanford. The focus is on developing a fully integrated system for grounded language understanding. So let’s dive in! The Stanford Colors and Context Task The core task in this … Read more

In this article, we will discuss how to implement a spam classifier using natural language processing techniques in Python. Spam classifiers are used to identify and classify spam messages, such as those found in emails or text messages. By building a model that can detect spam, we can effectively filter out unwanted messages and improve … Read more

In the realm of Natural Language Understanding (NLU), data resources play a crucial role in providing the foundation for analysis and investigation. To embark on a successful journey into this fascinating field, we must dive deep into the two primary types of data resources: the corpus and the knowledge base (KB). The Corpus: A Treasure … Read more

Have you ever wanted to train your own NLP models using pre-trained models? Look no further! In this guide, we will explore how to use the Hugging Face library to train NLP models for various tasks. Whether you want to perform sentiment analysis, sequence classification, or even generate text, Hugging Face has got you covered. … Read more

Welcome to the conclusion of the CS224U Natural Language Understanding course! We hope you’ve enjoyed this journey and found it both enjoyable and rewarding. As we come to the end, you may be wondering how you can continue building on what you’ve learned to conduct original research and develop new technologies. Well, the good news … Read more

Adversarial testing is an exciting approach that can reveal valuable insights about the systems we develop in the field of Natural Language Processing (NLP). While traditional evaluation methods in NLP involve creating a dataset from a single homogeneous process and assessing system performance based on a held-out test set, adversarial testing takes a different approach. … Read more

Are you interested in diving into the fascinating world of Natural Language Processing (NLP)? If so, then you’re in the right place! In this article, we’ll explore an essential NLP technique called Term Frequency-Inverse Document Frequency (TF-IDF) and its application in sentiment analysis. Preprocessing the Text Before we can apply TF-IDF, it is crucial to … Read more

Introduction: Natural Language Processing (NLP) is a fascinating field that involves developing techniques to process and analyze human language using computers. In this article, we will explore three broad categories of techniques that are commonly used in NLP to solve various problems. Whether you are a beginner or an experienced professional, this guide will provide … Read more

Welcome back, tech enthusiasts! Today, we dive into a fascinating topic: neural RSA, a powerful combination of the Rational Speech Acts (RSA) model and machine learning. In this article, we will explore how neural RSA can revolutionize various tasks, including image labeling, machine translation, problem-solving, and more. So, let’s jump right in! The Motivation Behind … Read more

Welcome to Techal! In this article, we will dive into the concept of Bag of Words, a key technique used in Natural Language Processing (NLP). NLP involves analyzing and deriving meaning from text data, and Bag of Words plays a crucial role in this process. What is Bag of Words? When working with text data, … Read more

Sentiment analysis, also known as opinion mining, is a crucial task in Natural Language Understanding (NLU). It involves classifying text into positive, negative, or neutral sentiment categories. In this article, we will explore some general practical tips and pre-processing techniques that can improve the accuracy of sentiment analysis models. Choosing Benchmark Data Sets and Sentiment … Read more

Welcome to Techal, where we explore the fascinating world of technology. Today, we delve into an essential concept in Natural Language Processing (NLP): TF-IDF. In this article, we will examine the intuition behind TF-IDF, the term frequency and inverse document frequency, and how they enable us to convert text into meaningful vectors. The Limitations of … Read more

Natural Language Inference (NLI) is a fascinating problem within the field of Natural Language Understanding (NLU). It involves determining whether a hypothesis statement can be inferred from a given premise statement. In other words, it is a classification task where we assign one of three labels – “entails,” “contradicts,” or “neutral” – to pairs of … Read more

Welcome to the world of natural language understanding! In this article, we’ll explore the groundbreaking ELECTRA model created by a team of researchers who are pushing the boundaries of contextual word representations. If you’re fascinated by the evolving field of technology and have a keen interest in language processing, then grab a cup of coffee … Read more

Whether you’re new to natural language processing (NLP) or an experienced practitioner, understanding tokenization is a crucial step to accurately analyze and process text data. In this tutorial, we’ll delve into the world of tokenization in spaCy, one of the most powerful NLP libraries available. Understanding the Basics of Tokenization Tokenization is the process of … Read more

Welcome, technology enthusiasts and engineers! Today, we’re going to delve into one of the most innovative and powerful applications of the transformer architecture in natural language understanding – the BERT model. Understanding the Core Model Structure of BERT At its core, BERT (Bidirectional Encoder Representations from Transformers) utilizes contextual word representations that leverage attention mechanisms … Read more

Welcome to the intriguing world of NLU (Natural Language Understanding) and information retrieval. In this article, we will explore the fascinating relationship between NLU and IR, and how these two fields can work together to enhance the capabilities of our NLU systems. What is Information Retrieval (IR)? Information retrieval, or IR, is a field concerned … Read more

Welcome to the third part of our series on analysis methods in NLP. In this article, we will delve into the fascinating world of adversarial training and testing of systems. Adversarial training is a method that involves using larger datasets to address cutting-edge questions and push the boundaries of natural language understanding. SWAG: Situations With … Read more

Welcome to the world of NLP conference submissions! If you’re an aspiring researcher or engineer in Natural Language Processing, submitting your work for publication can be both thrilling and agonizing. In this guide, we’ll walk you through the entire process and provide insights to help you navigate this journey successfully. The Anonymity Period Before we … Read more

Welcome to Techal, your trusted source for all things technology! In this article, we will delve into the fascinating realm of natural language understanding and explore how speakers bridge the gap between the physical world and language. Grounded Language Understanding In the field of natural language processing (NLP), grounded language understanding refers to the process … Read more

Welcome to part 2 of our series on Natural Language Understanding (NLU) and Information Retrieval (IR). In this installment, we will delve into classical IR and evaluation methods. Ranked Retrieval: The Simplest Form of IR Task In classical IR, one of the simplest tasks is ranked retrieval. This task involves providing a list of documents … Read more

Welcome to the world of Natural Language Processing (NLP). In this article, we will dive into two important concepts in NLP: stemming and lemmatization. The Need for Pre-processing Text Data In NLP, our input data is predominantly in the form of text. However, machine learning models cannot directly understand text data. Therefore, we need to … Read more

The rational speech acts model (RSA) is a groundbreaking development in language understanding. Developed by Stanford researchers Mike Frank and Noah Goodman, RSA connects concepts from cognitive psychology, linguistics, and machine learning to tackle large-scale problems. In this article, we will explore the core structure and high-level concepts of RSA. Understanding the RSA Model The … Read more

Welcome back, technology enthusiasts! In today’s article, we will delve into the world of classifier metrics. These metrics play a crucial role in evaluating the performance of classification models and allow us to gauge the accuracy and effectiveness of our systems. Confusion Matrices: The Building Blocks Before we deep dive into classifier metrics, let’s start … Read more

Word embeddings play a crucial role in Natural Language Processing (NLP), enabling machines to understand the meaning behind words and derive relationships between them. In this article, we will explore word embeddings in Gensim, a popular Python library primarily used for topic modeling. An Introduction to Gensim Gensim is a powerful NLP library similar to … Read more

Neural machine learning language translation is an exciting field that enables us to understand and communicate across different languages. In this tutorial, we will explore how to implement sequence-to-sequence learning using machine learning and Keras. By the end of this tutorial, you will have a solid understanding of how to build a language translation model. … Read more

Natural Language Understanding (NLU) has undergone a tremendous transformation over the years, leading us into a golden age of technological advancements. From its humble beginnings in the 1960s to the present day, NLU has evolved and matured, presenting new opportunities and challenges for researchers and practitioners alike. The Historical Journey of NLU The history of … Read more

Word embeddings play a crucial role in natural language processing (NLP) and text analysis. They are real-valued vectors that represent words and encode their meaning in a vector space. One popular technique for generating word embeddings is Word2Vec. According to the Wikipedia definition, “word embedding is a term used for the representation of words for … Read more

Do you want to train your own word vectors for Natural Language Processing (NLP)? Look no further than FastText, a popular word embedding technique. In this tutorial, we will explore how FastText works and how to train custom word vectors using it. What is FastText? FastText is a word embedding technique similar to Word2Vec, but … Read more

In the field of natural language understanding, evaluating the performance of models is crucial for iterative development. In this article, we will explore different evaluation techniques and metrics used to measure success in relation extraction tasks. So, let’s dive in and discover how we can effectively evaluate our models. Test-Driven Development Approach When starting a … Read more

Welcome to an exciting journey into the world of Natural Language Processing (NLP) using Python! In this tutorial series, we will dive deep into the fascinating field of NLP, exploring its applications, methodologies, and real-life use cases. Whether you are a technology enthusiast, a data scientist, or an aspiring NLP engineer, this comprehensive guide will … Read more

Welcome to an overview of Homework 2 on supervised sentiment analysis! In this article, we will explore the problem of cross-domain sentiment analysis and discuss the associated tasks and data. We will also delve into some baseline models and error analysis techniques that can help improve systems in this space. So, let’s dive in! Cross-Domain … Read more

Image Source: Techal Technology is advancing at an unprecedented rate, and with it, the capabilities of language models are also evolving. In recent years, language models such as OpenAI’s GPT-3 have made significant strides in understanding and generating human language. However, their impact is not limited to natural language processing alone. These powerful models have … Read more

Welcome, tech enthusiasts! In this article, we will dive into the world of fake news classification. For those who have been eagerly waiting, we’ll cover both machine learning and deep learning to tackle this problem. First, let’s start with two machine learning algorithms that work quite effectively. Getting the Dataset To begin with, we need … Read more

Welcome to Techal, where we dive deep into the world of technology. In this article, we’ll explore different modeling strategies for Natural Language Understanding (NLU). Whether you’re a language enthusiast or an engineer working on NLU projects, this article will provide valuable insights and strategies to enhance your understanding and improve your models. Hand-Built Feature … Read more

Welcome to part 2 of our series on presenting your research. In this installment, we will dive into the art of writing papers in the field of Natural Language Processing (NLP). Whether you’re a seasoned researcher or just starting out, understanding the structure and nuances of NLP papers is crucial for effectively communicating your ideas. … Read more

Welcome to the second episode of our “Zero to Hero with Natural Language Processing” series. In the previous video, we learned how to tokenize words using TensorFlow’s tools. Now, we will take that knowledge to the next level by creating sequences of numbers from our sentences and preparing them for teaching neural networks. Converting Sentences … Read more

Welcome to the world of artificial neural networks, where we dive into the fascinating realm of recurrent neural networks (RNNs). In this tutorial, we will explore the concept of back propagation in RNNs, building upon the knowledge we gained from forward propagation. Forward Propagation in RNNs To recap, in forward propagation, we provide an input … Read more

Welcome back to our series on Contextual Word Representations. In this installment, we delve into RoBERTa, a robustly optimized BERT approach. Inspired by the limitations of the original BERT model, the RoBERTa team set out to explore the uncharted territories in this space. Static vs. Dynamic Masking One of the central differences between BERT and … Read more

Neural Information Retrieval (IR) has revolutionized the field of search by significantly improving the quality of search results. In this article, we will explore the concept of neural IR and its impact on search engines. Query-Document Interaction: The Key to Neural IR One effective paradigm for building neural IR models is query-document interaction. This approach … Read more

Hello tech enthusiasts! Today, we will delve into the world of encoders and decoders and explore the challenges they present. In a previous video, we discussed how sequence-to-sequence learning with neural networks works, focusing on encoders and decoders. Now, let’s dive deeper into the problems associated with them. The Issue at Hand To begin, let’s … Read more

Welcome to an insightful exploration of analysis methods in Natural Language Processing (NLP). In this article, we will delve into various techniques that can enhance the performance and understanding of NLP models. So, buckle up and get ready to unlock the secrets behind effective analysis methods! Behavioral Evaluations: Adversarial Testing Adversarial testing is a flexible … Read more

Welcome back to the third part of our Zero to Hero series with TensorFlow. In the previous episodes, we learned how to tokenize text into numeric values and how to preprocess the text using tools in TensorFlow. Now, it’s time to dive into building a classifier to recognize sentiment in text. Dataset and Preprocessing To … Read more

Welcome back, technology enthusiasts! In this article, we’ll delve into the fascinating world of distributed word representations and explore various reweighting schemes. These schemes aim to amplify important associations while de-emphasizing mundane or erroneous ones, ultimately enhancing semantic information extraction. Importance of Reweighting Distributed word representations offer a powerful way to capture semantic relationships between … Read more

In the world of neural networks, backpropagation is a key algorithm that allows us to calculate gradients efficiently and accurately. By using the chain rule of calculus, backpropagation enables us to compute the gradients of the loss function with respect to the parameters of the neural network. This fundamental technique is vital for training neural … Read more

In today’s digital age, chatbots have become an essential tool for businesses to enhance customer service and engagement. Whether you’ve interacted with a chatbot on a mobile app or website, you’ve likely experienced their convenience and efficiency firsthand. In this article, we will delve into the world of chatbots, specifically focusing on natural language processing … Read more

In this tutorial, we will explore how to use fastText, a powerful library for text classification. We will be working with an e-commerce dataset, where each item is categorized based on its description. This tutorial is perfect for beginners who want to dive into Natural Language Processing (NLP) and learn how to classify text using … Read more

The field of Natural Language Processing (NLP) has experienced significant growth over the past decade, revolutionizing how we interact with technology. In this article, we will explore the reasons behind the current boom in NLP and how it is impacting various industries. The Rise of Industry Applications While NLP has been a part of academia … Read more

As technology enthusiasts and engineers, it is essential for us to understand the importance of model evaluation in our field. In this article, we will explore the various aspects of model evaluation and its role in conducting successful experiments. Why Baselines Matter When evaluating models in our field, it is crucial to consider baselines. Baselines … Read more

Welcome to the fifth episode of our Natural Language Processing with TensorFlow series. In this article, we will explore how to manage the understanding of context in language across longer sentences using a powerful technique called Long Short-Term Memory (LSTM). The Impact of Context in Language Understanding the impact of context in language is crucial. … Read more

Welcome to Techal, your go-to source for all things technology. In today’s article, we will dive into the architecture of recurrent neural networks (RNNs) and explore the concept of forward propagation over time. RNNs play a crucial role in various applications, particularly in natural language processing (NLP) tasks like sentiment analysis. By the end of … Read more

Text representation in Natural Language Processing (NLP) is a crucial step that involves extracting meaningful features from raw data. By representing text as numerical vectors, we enable machine learning models to understand and analyze textual information. In this guide, we will explore the basics of text representation and its significance in NLP. What Are Features … Read more