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awesome-nlp Awesome

A curated list of resources dedicated to Natural Language Processing

Maintainers - Keon Kim, Martin Park

Please read the contribution guidelines before contributing.

Please feel free to create pull requests, or email Martin Park(sp3005@nyu.edu) / Keon Kim (keon.kim@nyu.edu) to add links.

Table of Contents

Tutorials

Videos and Online Courses

Deep Learning and NLP

Word embeddings, RNNs, LSTMs and CNNs for Natural Language Processing

Classical NLP

Bayesian, statistics and Linguistics approaches for Natural Language Processing

Packages

Libraries

  • TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text

  • Node.js and Javascript - Node.js Libaries for NLP

    • Twitter-text - A JavaScript implementation of Twitter's text processing library
    • Knwl.js - A Natural Language Processor in JS
    • Retext - Extensible system for analyzing and manipulating natural language
    • NLP Compromise - Natural Language processing in the browser
    • Natural - general natural language facilities for node

  • Python - Python NLP Libraries

    • Scikit-learn: Machine learning in Python
    • Natural Language Toolkit (NLTK)
    • Pattern - A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
    • TextBlob - Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of NLTK and Pattern, and plays nicely with both.
    • YAlign - A sentence aligner, a friendly tool for extracting parallel sentences from comparable corpora.
    • jieba - Chinese Words Segmentation Utilities.
    • SnowNLP - A library for processing Chinese text.
    • KoNLPy - A Python package for Korean natural language processing.
    • Rosetta - Text processing tools and wrappers (e.g. Vowpal Wabbit)
    • BLLIP Parser - Python bindings for the BLLIP Natural Language Parser (also known as the Charniak-Johnson parser)
    • PyNLPl - Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for FoLiA, but also ARPA language models, Moses phrasetables, GIZA++ alignments.
    • python-ucto - Python binding to ucto (a unicode-aware rule-based tokenizer for various languages)
    • Parserator - A toolkit for making domain-specific probabilistic parsers
    • python-frog - Python binding to Frog, an NLP suite for Dutch. (pos tagging, lemmatisation, dependency parsing, NER)
    • python-zpar - Python bindings for ZPar, a statistical part-of-speech-tagger, constiuency parser, and dependency parser for English.
    • colibri-core - Python binding to C++ library for extracting and working with with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
    • spaCy - Industrial strength NLP with Python and Cython.
    • textacy - Higher level NLP built on spaCy
    • PyStanfordDependencies - Python interface for converting Penn Treebank trees to Stanford Dependencies.
    • gensim - Python library to conduct unsupervised semantic modelling from plain text
    • scattertext - Python library to produce d3 visualizations of how language differs between corpora.
    • CogComp-NlPy - Light-weight Python NLP annotators.
    • PyThaiNLP - Thai NLP in Python Package.
    • jPTDP - A toolkit for joint part-of-speech (POS) tagging and dependency parsing. jPTDP provides pre-trained models for 40+ languages.
    • CLTK: The Classical Language Toolkit is a Python library and collection of texts for doing NLP in ancient languages.
    • pymorphy2 - a good pos-tagger for Russian
    • BigARTM - a fast library for topic modelling
    • AllenNLP - An NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks.

  • C++ - C++ Libraries

    • MIT Information Extraction Toolkit - C, C++, and Python tools for named entity recognition and relation extraction
    • CRF++ - Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks.
    • CRFsuite - CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data.
    • BLLIP Parser - BLLIP Natural Language Parser (also known as the Charniak-Johnson parser)
    • colibri-core - C++ library, command line tools, and Python binding for extracting and working with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
    • ucto - Unicode-aware regular-expression based tokenizer for various languages. Tool and C++ library. Supports FoLiA format.
    • libfolia - C++ library for the FoLiA format
    • frog - Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.
    • MeTA - MeTA : ModErn Text Analysis is a C++ Data Sciences Toolkit that facilitates mining big text data.
    • Mecab (Japanese)
    • Mecab (Korean)
    • Moses
    • StarSpace - a library from Facebook for creating embeddings of word-level, paragraph-level, document-level and for text classification

  • Java - Java NLP Libraries

    • Stanford NLP
    • OpenNLP
    • ClearNLP
    • Word2vec in Java
    • ReVerb Web-Scale Open Information Extraction
    • OpenRegex An efficient and flexible token-based regular expression language and engine.
    • CogcompNLP - Core libraries developed in the U of Illinois' Cognitive Computation Group.
    • MALLET - MAchine Learning for LanguagE Toolkit - package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
    • RDRPOSTagger - A robust POS tagging toolkit available (in both Java & Python) together with pre-trained models for 40+ languages.

  • Scala - Scala NLP Libraries

    • Saul - Library for developing NLP systems, including built in modules like SRL, POS, etc.
    • ATR4S - Toolkit with state-of-the-art automatic term recognition methods.
    • tm - Implementation of topic modeling based on regularized multilingual PLSA.
    • word2vec-scala - Scala interface to word2vec model; includes operations on vectors like word-distance and word-analogy.
    • Epic - Epic is a high performance statistical parser written in Scala, along with a framework for building complex structured prediction models.

  • R - R NLP Libraries

    • text2vec - Fast vectorization, topic modeling, distances and GloVe word embeddings in R.
    • wordVectors - An R package for creating and exploring word2vec and other word embedding models
    • RMallet - R package to interface with the Java machine learning tool MALLET
    • dfr-browser - Creates d3 visualizations for browsing topic models of text in a web browser.
    • dfrtopics - R package for exploring topic models of text.
    • sentiment_classifier - Sentiment Classification using Word Sense Disambiguation and WordNet Reader
    • jProcessing - Japanese Natural Langauge Processing Libraries, with Japanese sentiment classification

  • Clojure

    • Clojure-openNLP - Natural Language Processing in Clojure (opennlp)
    • Infections-clj - Rails-like inflection library for Clojure and ClojureScript
    • postagga - A library to parse natural language in Clojure and ClojureScript

  • Ruby

  • Rust

    • whatlang — Natural language recognition library based on trigrams

Services

Techniques

Text Embeddings

Text embeddings allow deep learning to be effective on smaller datasets. These are often first inputs to a deep learning archiectures and most popular way of transfer learning in NLP. Embeddings are simply vectors or a more generically, real valued representations of strings. Word embeddings are considered a great starting point for most deep NLP tasks.

The most popular names in word embeddings are word2vec by Google (Mikolov) and GloVe by Stanford (Pennington, Socher and Manning). fastText seems to be a fairly popular for multi-lingual sub-word embeddings.

word2vec

word2vec was introduced by T. Mikolov et al. when he was with Google. Performs well on word similarity and analogy tasks

GloVe

GloVe was introduced by Pennington, Socher, Manning from Stanford in 2014 as a statistical approximation to word embeddings. The word vectors are created by matrix factorizations of word-word co-occurence matrices here.

fastText

fastText by Mikolov (from Facebook) supports sub-word embeddings in more than 200 languages. This allows it to work with out of vocabulary words as well. It captures language morphology well. It also supports a supervised classification mechanism.

Other Text Embeddings

Thought Vectors

Thought vectors are numeric representations for sentences, paragraphs, and documents. The following papers are listed in order of date published, each one replaces the last as the state of the art in sentiment analysis.

Machine Translation

Single Exchange Dialogs

A Neural Network Approach toContext-Sensitive Generation of Conversational Responses Sordoni 2015. Generates responses to tweets. Uses Recurrent Neural Network Language Model (RLM) architecture of (Mikolov et al., 2010). source code: RNNLM Toolkit

Neural Responding Machine for Short-Text Conversation Shang et al. 2015 Uses Neural Responding Machine. Trained on Weibo dataset. Achieves one round conversations with 75% appropriate responses.

A Neural Conversation Model Vinyals, Le 2015. Uses LSTM RNNs to generate conversational responses. Uses seq2seq framework. Seq2Seq was originally designed for machine transation and it "translates" a single sentence, up to around 79 words, to a single sentence response, and has no memory of previous dialog exchanges. Used in Google Smart Reply feature for Inbox

Memory and Attention Models

Most are courtesy andrewt3000/DL4NLP

General Natural Language Processing

Named Entity Recognition

Neural Network

Supplementary Materials

Blogs

Review Articles

Datasets

Credits

part of the lists are from

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