awesomelistsio/awesome-nlp

A curated list of awesome frameworks, libraries, tools, datasets, tutorials, and research papers for Natural Language Processing (NLP). This list covers a variety of NLP tasks, from text processing and tokenization to state-of-the-art language models and applications like sentiment analysis and machine translation.

Awesome Natural Language Processing (NLP)

     

A curated list of awesome frameworks, libraries, tools, datasets, tutorials, and research papers for Natural Language Processing (NLP). This list covers a variety of NLP tasks, from text processing and tokenization to state-of-the-art language models and applications like sentiment analysis and machine translation.

Contents

• Frameworks and Libraries
• Text Processing and Tokenization
• Pretrained Language Models
• NLP Tasks
• Tools and Applications
• Datasets
• Research Papers
• Learning Resources
• Books
• Community
• Contribute
• License

Frameworks and Libraries

• Hugging Face Transformers - A comprehensive library of state-of-the-art NLP models like BERT, GPT, and RoBERTa.
• spaCy - An open-source library for advanced natural language processing in Python.
• NLTK (Natural Language Toolkit) - A comprehensive library for text processing and analysis.
• Stanford NLP - A suite of NLP tools developed by the Stanford NLP Group.
• AllenNLP - An open-source NLP research library built on top of PyTorch.
• TextBlob - A simple library for processing textual data in Python.

Text Processing and Tokenization

• Moses Tokenizer - A widely used tokenizer for machine translation tasks.
• BPE (Byte Pair Encoding) - A subword tokenization technique used by models like GPT and BERT.
• SentencePiece - A language-independent tokenization and text processing library.
• RegexpTokenizer (NLTK) - A tokenizer that uses regular expressions to split text into tokens.
• spaCy Tokenizer - A fast and efficient tokenizer integrated within the spaCy library.

Pretrained Language Models

• BERT (Bidirectional Encoder Representations from Transformers) - A Transformer-based model for a variety of NLP tasks.
• GPT-3 (Generative Pre-trained Transformer 3) - A powerful generative language model by OpenAI.
• RoBERTa - An optimized variant of BERT, focusing on robustly optimized pretraining.
• T5 (Text-to-Text Transfer Transformer) - A model that treats every NLP task as a text-to-text problem.
• XLNet - A generalized autoregressive pretraining model that outperforms BERT on several tasks.
• DistilBERT - A smaller, faster, and lighter version of BERT.

NLP Tasks

• Sentiment Analysis: The process of determining the sentiment (positive, negative, or neutral) of a text.
  • TextBlob Sentiment Analysis
  • VADER Sentiment Analysis
• Named Entity Recognition (NER): Identifying and classifying entities in text (e.g., names, dates).
  • spaCy NER
  • Stanford NER
• Machine Translation: Translating text from one language to another.
  • OpenNMT - A neural machine translation framework.
  • Fairseq - A Facebook AI research framework for sequence-to-sequence models.
• Text Summarization: Generating a concise summary of a given text.
  • BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation
  • PEGASUS - A pre-trained model specifically designed for text summarization.

Tools and Applications

• Gensim - A Python library for topic modeling and document similarity.
• Stanford CoreNLP - A suite of NLP tools for linguistic analysis.
• FastText - A library for efficient text classification and representation learning.
• Polyglot - A multilingual NLP toolkit supporting various languages.
• LexRank - A text summarization library using graph-based ranking algorithms.

Datasets

• GLUE Benchmark - A collection of resources for evaluating natural language understanding systems.
• SQuAD (Stanford Question Answering Dataset) - A dataset for reading comprehension and question answering tasks.
• CoNLL-2003 - A dataset for named entity recognition.
• IMDB Reviews - A dataset for sentiment analysis.
• WikiText - A collection of high-quality text from Wikipedia for language modeling tasks.

Research Papers

• Attention Is All You Need (2017) - The paper that introduced the Transformer architecture, revolutionizing NLP.
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018) - The introduction of the BERT model.
• GloVe: Global Vectors for Word Representation (2014) - A model for generating word embeddings.
• Word2Vec: Efficient Estimation of Word Representations in Vector Space (2013) - The introduction of Word2Vec, a method for learning word embeddings.
• ELMo: Deep Contextualized Word Representations (2018) - A model for contextual word embeddings.

Learning Resources

• Coursera: Natural Language Processing Specialization - A comprehensive course on NLP by Deeplearning.ai.
• Stanford CS224N: Natural Language Processing with Deep Learning - A popular university course on NLP.
• Fast.ai NLP Course - A practical course on NLP using the fastai library.
• Hugging Face Tutorials - Official tutorials for using the Hugging Face NLP library.

Books

• Speech and Language Processing by Daniel Jurafsky and James H. Martin - A comprehensive textbook on NLP.
• Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper - An introduction to NLP using Python.
• Deep Learning for Natural Language Processing by Palash Goyal, Sumit Pandey, and Karan Jain - A book covering deep learning techniques in NLP.

Community

• Reddit: r/NLP - A subreddit for discussions on natural language processing.
• Hugging Face Community - A forum for discussing the Hugging Face NLP library.
• NLP Summit - An annual conference focused on NLP research and applications.

Contribute

Contributions are welcome!

License