If you like our work, please consider supporting us so we can keep doing what we do. And as a current subscriber, enjoy this nice discount!
Also: if you haven’t yet, follow us on Twitter, TikTok, or YouTube!
Whereas we have covered various aspects of NLP, one of the most common queries has been to see what the various tasks can be accomplished using NLP. Today's post is about those tasks.
There are many common natural processing tasks that can be performed in order to clean and prepare data for further analysis. Below are some of the most common tasks:
- Tokenization: This is the process of breaking down a string of text into smaller pieces, called tokens. Tokenization is often used to strip out punctuation and other unwanted characters from text data.
- Stopword removal: This is the process of removing words that are commonly used but don't carry much meaning, such as "a", "the", "and", etc. Stopword removal is often used to improve the accuracy of text classification and other machine learning tasks.
- Stemming and lemmatization: These are two related processes that are used to reduce words to their base form. Stemming is a more aggressive process that simply chops off any suffixes or prefixes, while lemmatization also takes into account the meaning of the word and tries to find the base form of the word.
- Part-of-speech tagging: This is the process of assigning a part of speech to each token, such as noun, verb, adjective, etc. Part-of-speech tagging is often used to improve the accuracy of text classification and other machine learning tasks.
- Named entity recognition: This is the process of identifying named entities in text, such as people, places, organizations, etc. Named entity recognition is often used in information extraction and question answering systems.
- Word sense disambiguation: This is the process of assigning the correct sense of a word to a context. For example, the word "bank" can refer to a financial institution or the side of a river. Word sense disambiguation is often used in information extraction and question answering systems.
- Sentiment analysis: This is the process of identifying the sentiment of a text, such as positive, negative, or neutral. Sentiment analysis is often used to gauge the public opinion of a company or product.
- Text classification: This is the process of assigning a class label to a text, such as spam or not spam. Text classification is often used in spam filtering and other applications.
- Language detection: This is the process of identifying the language of a text. Language detection is often used in machine translation and other applications.
From the above tasks, you can use them to construct use cases within your organization and see if technology can help you cover the use case.
In the upcoming posts, we will resume linguists and then cover ELMO, Transformers, GPT2/3 and other advanced topics in the domain of NLP.
Do you like our work?
Consider becoming a paying subscriber to support us!
