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In the field of Natural Language Processing (NLP), one of the fundamental tasks is Parts of Speech (PoS) tagging. PoS tagging involves assigning grammatical categories, like nouns, verbs, adjectives, etc., to words in a sentence. This process plays an important role in many NLP applications, including text analysis, information retrieval,...
Guest Contributor
TextBlob is a package built on top of two other packages, one of them is called Natural Language Toolkit, known mainly in its abbreviated form as NLTK, and the other is Pattern. NLTK is a traditional package used for text processing or Natural Language Processing (NLP), and Pattern is built...
Cássia Sampaio
In today's digital world, there is a vast amount of text data created and transferred in the form of news, tweets, and social media posts. Can you imagine the time and effort needed to process them manually? Fortunately, Natural Language Processing (NLP) techniques help us manipulate, analyze, and interpret text...
Shri Varsheni
There are plenty of guides explaining how transformers work, and for building an intuition on a key element of them - token and position embedding. Positionally embedding tokens allowed transformers to represent non-rigid relationships between tokens (usually, words), which is much better at modeling our context-driven speech in language modeling....
David Landup
Transformers, even though released in 2017, have only started gaining significant traction in the last couple of years. With the proliferation of the technology through platforms like HuggingFace, NLP and Large Language Models (LLMs) have become more accessible than ever. Yet - even with all the hype around them and...
Spelling mistakes are common, and most people are used to software indicating if a mistake was made. From autocorrect on our phones, to red underlining in text editors, spell checking is an essential feature for many different products. The first program to implement spell checking was written in 1971 for...
Kristina Popovic
The constant growth of data on the Internet creates a demand for tools that process textual information. Moreover, it's highly important that this instrument of text analysis could implement solutions for both low and high-level NLP tasks such as counting word frequencies, calculating sentiment analysis of the texts or detecting...
Natalia Kuzminykh
State-of-the-art technologies in NLP allow us to analyze natural languages on different layers: from simple segmentation of textual information to more sophisticated methods of sentiment categorizations. However, it does not inevitably mean that you should be highly advanced in programming to implement high-level tasks such as sentiment analysis in Python....
The amount of textual data on the Internet has significantly increased in the past decades. There's no doubt that the processing of this amount of information must be automated, and the TextBlob package is one of the fairly simple ways to perform NLP - Natural Language Processing. It provides a...
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