awesome-nlp

 


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

Table of Contents

Tutorials and Courses

  • Tensor Flow Tutorial on Seq2Seq Models
  • Natural Language Understanding with Distributed Representation Lecture Note by Cho

videos

Deep Learning for NLP

Stanford CS 224D: Deep Learning for NLP class
Class by Richard Socher. 2016 content was updated to make use of Tensorflow. Lecture slides and reading materials for 2016 class here. Videos for 2016 class here. Note that there are some lecture videos missing for 2016 (lecture 9, and lectures 12 onwards). All videos for 2015 class here

Udacity Deep Learning Deep Learning course on Udacity (using Tensorflow) which covers a section on using deep learning for NLP tasks. This section covers how to implement Word2Vec, RNN's and LSTMs.

A Primer on Neural Network Models for Natural Language Processing
Yoav Goldberg. October 2015. No new info, 75 page summary of state of the art.

Packages

Implementations

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)
    • 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.
    • PyStanfordDependencies - Python interface for converting Penn Treebank trees to Stanford Dependencies.

  • 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

  • Java - Java NLP Libraries

  • Clojure

    • Clojure-openNLP - Natural Language Processing in Clojure (opennlp)
    • Infections-clj - Rails-like inflection library for Clojure and ClojureScript

  • Ruby

Services

  • Wit-ai - Natural Language Interface for apps and devices.

Articles

Review Articles

Word Vectors

Resources about word vectors, aka word embeddings, and distributed representations for words.
Word vectors are numeric representations of words that are often used as input to deep learning systems. This process is sometimes called pretraining.

Efficient Estimation of Word Representations in Vector Space
Distributed Representations of Words and Phrases and their Compositionality
Mikolov et al. 2013.
Generate word and phrase vectors. Performs well on word similarity and analogy task and includes Word2Vec source codeSubsamples frequent words. (i.e. frequent words like "the" are skipped periodically to speed things up and improve vector for less frequently used words)
Word2Vec tutorial in TensorFlow

Deep Learning, NLP, and Representations
Chris Olah (2014) Blog post explaining word2vec.

GloVe: Global vectors for word representation
Pennington, Socher, Manning. 2014. Creates word vectors and relates word2vec to matrix factorizations. Evalutaion section led to controversy by Yoav Goldberg
Glove source code and training data

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.

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher et al. 2013. Introduces Recursive Neural Tensor Network. Uses a parse tree.

Distributed Representations of Sentences and Documents
Le, Mikolov. 2014. Introduces Paragraph Vector. Concatenates and averages pretrained, fixed word vectors to create vectors for sentences, paragraphs and documents. Also known as paragraph2vec. Doesn't use a parse tree.
Implemented in gensim. See doc2vec tutorial

Deep Recursive Neural Networks for Compositionality in Language
Irsoy & Cardie. 2014. Uses Deep Recursive Neural Networks. Uses a parse tree.

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Tai et al. 2015 Introduces Tree LSTM. Uses a parse tree.

Semi-supervised Sequence Learning
Dai, Le 2015 "With pretraining, we are able to train long short term memory recurrent networks up to a few hundred timesteps, thereby achieving strong performance in many text classification tasks, such as IMDB, DBpedia and 20 Newsgroups."

Machine Translation

Neural Machine Translation by jointly learning to align and translate Bahdanau, Cho 2014. "comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation." Implements attention mechanism.
English to French Demo

Sequence to Sequence Learning with Neural Networks
Sutskever, Vinyals, Le 2014. (nips presentation). Uses LSTM RNNs to generate translations. " Our main result is that on an English to French translation task from the WMT’14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8"
seq2seq tutorial in

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 (from DL4NLP)

Reasoning, Attention and Memory RAM workshop at NIPS 2015. slides included

Memory Networks Weston et. al 2014, and End-To-End Memory Networks Sukhbaatar et. al 2015.
Memory networks are implemented in MemNN. Attempts to solve task of reason attention and memory.
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Weston 2015. Classifies QA tasks like single factoid, yes/no etc. Extends memory networks.
Evaluating prerequisite qualities for learning end to end dialog systems
Dodge et. al 2015. Tests Memory Networks on 4 tasks including reddit dialog task.
See Jason Weston lecture on MemNN

Neural Turing Machines
Graves et al. 2014.

Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
Joulin, Mikolov 2015. Stack RNN source code and blog post

General Natural Language Processing

Named Entity Recognition

Neural Network

Supplementary Materials

Blogs

Credits

part of the lists are from

 
posted @ 2016-08-22 23:09  AHU-WangXiao  阅读(667)  评论(0)  编辑  收藏  举报