Common NLP Tasks

NLP, or Natural Language Processing, includes a diverse range of language-related tasks aimed at understanding and processing human language.

Some common NLP tasks include:

Sentiment analysis

Determining the sentiment or emotion expressed in a piece of text, such as positive, negative, or neutral.

Toxicity classification

Identifying and categorizing offensive, hateful, or misleading content, including hate speech, defamation, fake news, and misinformation.

Machine translation

Translating text from one language to another automatically using computational algorithms.

Named entity recognition

Identifying and categorizing named entities, such as people, organizations, and locations, mentioned in text.

Spam detection

Automatically detecting and filtering out unwanted or unsolicited messages, such as email spam or social media spam.

Grammatical error correction

Automatically identifying and correcting grammatical errors in text.

Topic modeling

Extracting topics or themes from a collection of documents to discover underlying patterns or trends.

Text generation

Generating text automatically, including autocomplete suggestions and conversational responses for chatbots.

Text summarization

Condensing longer pieces of text into shorter summaries, either through extractive methods (selecting and combining key sentences) or abstractive methods (generating new sentences).

Question answering

Automatically generating answers to questions posed in natural language based on a given context.

These tasks form the foundation of NLP research and applications, with repositories like Kaggle and Papers with Code serving as valuable resources for datasets, codes, and research contributions in specific areas of NLP.