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NLP tasks

Natural Language Processing (NLP) encompasses a variety of tasks that enable machines to understand, interpret, and generate human language. Below is an overview of some of the most important NLP tasks and their applications.

1. Sentiment Analysis

Description: Determines the sentiment or emotion expressed in text (positive, negative, neutral).
Applications: Social media monitoring, product reviews, customer feedback.
Datasets: IMDB Reviews, Sentiment140.
References:

2. Named Entity Recognition (NER)

Description: Identifies entities in text such as names of people, organizations, locations, etc.
Applications: Information extraction, legal document analysis, bioinformatics.
Datasets: CoNLL-2003, OntoNotes.
References:

3. Machine Translation

Description: Automatically translates text from one language to another.
Applications: Cross-lingual communication, global business, localization.
Datasets: WMT, OpenSubtitles.
References:

4. Text Summarization

Description: Produces a concise summary of a longer text while retaining essential information.
Applications: News summarization, legal briefings, academic research.
Datasets: CNN/DailyMail, XSum.
References:

5. Text Classification

Description: Categorizes text into predefined classes (e.g., spam detection, topic classification).
Applications: Email filtering, topic modeling, document organization.
Datasets: AG News, Reuters-21578.
References:

6. Question Answering (QA)

Description: Answers questions based on a given text passage or context.
Applications: Chatbots, search engines, educational tools.
Datasets: SQuAD, TriviaQA.
References:

7. Language Modeling

Description: Predicts the next word or sequence of words in a text.
Applications: Autocomplete, text generation, conversational AI.
Datasets: WikiText, Penn Treebank.
References:

8. Part-of-Speech (POS) Tagging

Description: Assigns grammatical categories (e.g., noun, verb, adjective) to words in a sentence.
Applications: Grammar correction, syntactic analysis, linguistic research.
Datasets: Universal Dependencies, WSJ Corpus.
References:

9. Coreference Resolution (CR)

Description: CR17 Identifies and links all expressions in a text that refer to the same entity.
Applications: Dialogue systems, summarization, information extraction.
Datasets: OntoNotes, CoNLL-2012, TED Talks Dataset, Europarl Dataset
References:

Footnotes

  1. 1.GLUE - The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems. ↩
  2. 2.IMDB Dataset - Large Movie Review Dataset for Sentiment Analysis by Stanford. ↩
  3. 3.CoNLL-2003 - Dataset for Named Entity Recognition and other sequence modeling tasks. ↩
  4. 4.OntoNotes - Annotated dataset covering various linguistic phenomena. ↩
  5. 5.WMT - Workshop on Machine Translation datasets for translation tasks. ↩
  6. 6.OpenSubtitles - Large corpus of subtitles for multilingual tasks. ↩
  7. 7.CNN/DailyMail - Dataset for abstractive summarization tasks. ↩
  8. 8.XSum - Dataset for extreme summarization of news articles. ↩
  9. 9.AG News - News topic classification dataset. ↩
  10. 10.Reuters-21578 - Text categorization benchmark dataset. ↩
  11. 11.SQuAD - Stanford Question Answering Dataset for reading comprehension tasks. ↩
  12. 12.TriviaQA - Dataset containing trivia questions and evidence passages. ↩
  13. 13.WikiText - Dataset for language modeling based on Wikipedia articles. ↩
  14. 14.Penn Treebank - Corpus for linguistic annotation and modeling. ↩
  15. 15.Universal Dependencies - Framework for consistent grammatical annotation across languages. ↩
  16. 16.WSJ Corpus - Dataset from Wall Street Journal articles for POS tagging. ↩
  17. 17.Coreference resolution (CR) is the task of finding all linguistic expressions (called mentions) in a given text that refer to the same real-world entity. ↩
  18. 18.CoNLL-2012 - Shared task on coreference resolution and other NLP challenges. ↩
  19. 19.Annotated dataset created to evaluate RoBERTa’s performance on coreference tasks, with a focus on contextual embeddings. ↩