What is Part-of-speech (POS) tagging? Part-of-speech (POS) tagging is fundamental in natural language processing (NLP) and can be done in Python. It involves labelling words in a sentence with their...

What is Part-of-speech (POS) tagging? Part-of-speech (POS) tagging is fundamental in natural language processing (NLP) and can be done in Python. It involves labelling words in a sentence with their...
Transformers Implementations in TensorFlow, PyTorch, Hugging Face and OpenAI's GPT-3 What are transformers in natural language processing? Natural language processing (NLP) is a field of artificial...
What is a question-answering System? Question answering (QA) is a field of natural language processing (NLP) and artificial intelligence (AI) that aims to develop systems that can understand and...
What is the curse of variability? The curse of variability refers to the idea that as the variability of a dataset increases, the difficulty of finding a good model that can accurately predict...
What is a Siamese network? It is also commonly known as one or few-shot learning. They are popular because less labelled data is required to train them. Siamese networks are often used to figure out...
What exactly is text clustering? The process of grouping a collection of texts into clusters based on how similar their content is is known as text clustering. Text clustering combines related...
Opinion mining is a field that is growing quickly. It uses natural language processing and text analysis to gather subjective information from sources. The main goal of opinion mining is to find and...
Introduction to document clustering and its importance Grouping similar documents together in Python based on their content is called document clustering, also known as text clustering. This...
What is local sensitive hashing? A technique for performing a rough nearest neighbour search in high-dimensional spaces is called local sensitive hashing (LSH). It operates by mapping...
Categorical variables are variables that can take on one of a limited number of values. These variables are commonly found in datasets and can't be used directly in machine learning models as most...
Numerous tasks in natural language processing (NLP) depend heavily on an attention mechanism. When the data is being processed, they allow the model to focus on only certain input elements, such as...
Long Short-Term Memory (LSTM) is a powerful natural language processing (NLP) technique. This powerful algorithm can learn and understand sequential data, making it ideal for analyzing text and...
Convolutional Neural Networks (CNN) are a type of deep learning model that is particularly well-suited for tasks that involve working with structured data, such as images, audio, or text in NLP....
Best RNN For NLP: Elman RNNs, Long short-term memory (LSTM) networks, Gated recurrent units (GRUs), Bi-directional RNNs and Transformer networks What is an RNN? A recurrent neural network (RNN) is...
Encoder, decoder and encoder-decoder transformers are a type of neural network currently at the bleeding edge in NLP. This article explains the difference between these architectures and what they...
What is a Hidden Markov Model in NLP? A time series of observations, such as a Hidden Markov Model (HMM), can be represented statistically as a probabilistic model. Natural language processing (NLP)...
What is deep learning for natural language processing? Deep learning is a part of machine learning based on how the brain works, especially the neural networks that make up the brain. It requires...
Neural machine translation (NMT) is a state-of-the-art technique for translation. Our previous article on translating text in Python covered the two most common ways of getting started with...
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