Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
text_mining_resources
Resources for learning about Text Mining and Natural Language Processing
https://github.com/stepthom/text_mining_resources
- Text Mining with R
- Mastering Text Mining with R
- Text Mining in Practice with R
- Natural Language Processing with Transformers, Revised Edition
- Getting Started with Natural Language Processing
- Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (NLP) Applications
- Practical Natural Language Processing
- Natural Language Processing with Python
- Natural Language Processing with PyTorch
- Python Natural Language Processing
- Mastering Natural Language Processing with Python
- Natural Language Processing: Python and NLTK
- Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
- Applied Natural Language Processing With Python
- Deep Learning with Text
- Taming Text: How to Find, Organize, and Manipulate It - on guide to learn innovative tools and techniques for finding, organizing, and manipulating unstructured text.
- Speech and Language Processing
- Foundations of Statistical Natural Language Processing
- Language Processing with Perl and Prolog: Theories, Implementation, and Application (Cognitive Technologies)
- An introduction for information retrieval
- Handbook of Natural Language Processing
- Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
- Fundamentals of Predictive Text Mining
- Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More
- Neural Network Methods for Natural Language Processing
- Text Mining: A Guidebook for the Social Sciences
- Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence
- Neural Network Methods in Natural Language Processing
- Machine Learning for Text (2018)
- Natural Language Processing in Spanish
- Foundations of Computational Linguistics Human-Computer Communication in Natural Language
- Statistical Methods for Speech Recognition
- How To Label Data
- Probably Approximately a Scientific Blog
- Sebastian Ruder
- NLP-progress
- Natural Language Processing Blog
- NLP in healthcare
- AI Harvard Business Review
- Why Accuracy in Natural Language Processing is Crucial to the Future of AI in Retail
- Natural Language Processing is Fun! How computers understand Human Language
- WEF Live Campaign - Twitter fed Global News Topics & Sentiment Tracker - Live Jan 2019
- Modern Deep Learning Techniques Applied to Natural Language Processing
- The Definitive Guide to Natural Language Processing
- From Natural Language to Calendar Entries, with Clojure
- Ask HN: How Can I Get into NLP (Natural Language Processing)?
- Ask HN: What are the best tools for analyzing large bodies of text?
- Quora: How do I learn Natural Language Processing?
- Quora Topic: Natural Language Processing
- The Definitive Guide to Natural Language Processing
- Futures of text
- R or Python on Text Mining
- Where to start in Text Mining
- Text Mining in R and Python: 8 Tips To Get Started
- An introduction to text analysis with Python, Part 1
- Mining Twitter Data with Python (Part 1: Collecting Data)
- Why Text Mining May Be The Next Big Thing
- SAS CEO offers analytics over BI, reveals use cases for text analytics
- Value and benefits of text mining
- Text Mining South Park - A Text Mining blog which covers on a variety of topics.
- Natural Language Processing: An Introduction
- Natural Language Processing Tutorial
- Natural Language Processing blog
- An Introduction to Text Mining using Twitter Streaming API and Python
- How To Get Into Natural Language Processing
- Betty: a friendly English-like interface for your command line.
- Creating machine learning models to analyze startup news - Part1 - machine-learning-models-analyze-news/). [Part 3](https://monkeylearn.com/blog/analyzing-startup-news-with-machine-learning/).
- Comparison of the Most Useful Text Processing APIs
- 100 Must-Read NLP Papers
- Python Guide for dealing with Text Data
- Crowdsourcing Ground Truth for Medical Relation Extraction
- Natural language based financial forecasting: a survey
- Natural language based financial forecasting: a survey
- 5 Heroic Tools for Natural Language Processing
- Natural Language Processing unlocks hidden data to transform healthcare efficiency, quality and cost
- Extracting medical problems from electronic clinical documents
- Natural Language Processing (NLP) for Machine Learning
- How to Write a Spelling Corrector - by Peter Norvig
- Using AI to unleash the power of unstructured government data - to-comprehend primer and background on NLP, and the various applications NLP could be used on unstructured Government text data. The article includes many US Government examples on how NLP is currently deployed across different domains (e.g. to help analyze public feedback/sentiment analysis/topic modelling, to improve forensic investigations, to aid in Government policy-making and regulatory compliance). The key point is to apply different NLP techniques to explore and uncover key Government intelligence insights.
- Extracting Features of Entertainment Products: A Guided Latent Dirichlet Allocation Approach Informed by the Psychology of Media Consumption - level consumption.” This academic article provides both a framework and managerial implications that suggest the application of LDA and NLP for feature extraction in entertainment products that can aid in traditional content-based consumer behavior models, and relevant marketing models applied to the media and entertainment industry.
- Lessons learned building natural language processing systems in health care
- How Algorithms Know What You’ll Type Next
- AI bias: It is the responsibility of humans to ensure fairness
- Venturebeat Blogpost - Gender biases in datasets - Based on UCLA research paper "Learning Gender Neutral Word Embeddings" Aug 2018.
- Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
- Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
- Scraping HTML using Scrapy
- Extract text from any document; no muss, no fuss.
- Using Scrapy to Build your Own Dataset
- How to solve 90% of NLP problems: a step-by-step guide
- Text Preprocessing in Python: Steps, Tools, and Examples
- How to Clean Text for Machine Learning with Python - by-step guide of how to perform text data pre-processing.
- Feature Extraction, Basic Pre-processing, and Advanced Processing
- Removing stop words with NLTK in Python
- TEXT CLASSIFICATION FOR SENTIMENT ANALYSIS – STOPWORDS AND COLLOCATIONS
- Article: Text Stemming: Approaches, Applications, and Challenges
- What is the Difference Between Stemming and Lemmatization?
- Stemming and Lemmatization in Python
- Sentiment Symposium Tutorial: Stemming
- Taming Text with the SVD
- Dimensionality Reduction for Bag-of-Words Models: PCA vs LSA
- An introduction to Bag of Words and how to code it in Python for NLP
- Bag of Words and Tf-idf Explained
- Automatic Sarcasm Detection: A Survey
- CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
- A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
- Detecting Sarcasm with Deep Convolutional Neural Networks
- Naive Bayes and Text Classification - depth overview of both the Naive Bayes algorithm and how it can be used in the document classification process.
- Bag of Tricks for Efficient Text Classification
- Text Classifier Algorithms in Machine Learning
- Classifying Documents in the Reuters-21578 R8 Dataset
- Tidy Text Mining Beer Reviews
- Using fastText and Comet.ml to classify relationships in Knowledge Graphs
- Multi-Class Text Classification with Scikit-Learn - class problems, such as classifying consumer complaints into one of 12 categories.
- Machine Learning with Text in scikit-learn (PyCon 2016) - learn in the document classification process.
- Ultimate guide to deal with Text Data (using Python) – for Data Scientists & Engineers
- Text Classification in Python with scikit-learn and nltk - learn.
- Introducing state of the art text classification with universal language models
- Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews - paper with code on Github
- Towards Explainable NLP: A Generative Explanation Framework for Text Classification
- Entity Extraction and Network Analysis
- Natural Language Processing for Information Extraction
- NLP Techniques for Extracting Information - depth exploration of the seven steps framework of NLP data mining tools and techniques.
- Text Clustering: Get quick insights from Unstructured Data
- Document Clustering
- Document Clustering: A Detailed Review
- Document Clustering with Python
- Text mining and sentiment analysis on video game user reviews using SAS® Enterprise Miner
- Who wrote the anti-Trump New York Times op-ed? Using tidytext to find document similarity
- Topic models: Past, present, and future
- Word vectors using LSA, Part - 2
- Probabilistic Topic Models
- LEGO color themes as topic models
- How our startup switched from Unsupervised LDA to Semi-Supervised GuidedLDA
- Topic Modeling with LSA, PLSA, LDA & lda2Vec
- text2vec's Description of Topic Models
- Topic Modelling Portal
- Applications of Topic Models
- MACS 30500: Text analysis: topic modeling
- COTA, Uber’s topic modelling approach to improving customer support
- Using LDA Topic Models as a Classification Model Input
- NLP: Extracting the main topics from your dataset using LDA in minutes
- Topic Modelling The Legal Subject Matter And Judicial Activity Of The High Court Of Australia, 1903–2015
- CACM: Techniques and Applications for Sentiment Analysis
- Unsupervised Sentiment Analysis with Signed Social Networks
- Lexicon-Based Methods for Sentiment Analysis - CAL (Semantic Orientation CALculator), a measure of subjectivity and opinion for sentimental analysis.
- That Sentimental Feeling - syuzhet-validation/).
- Unsupervised Sentiment Neuron
- Current State of Text Sentiment Analysis from Opinion to Emotion Mining
- Sentiment Analysis Tools Overview, Part 1. Positive and Negative Words Databases
- Sentiment analysis, Concept analysis and Applications
- Breakthrough Research Papers and Models for Sentiment Analysis
- Twitter sentiment analysis using combined LSTM-CNN models
- VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text - based model of sentiment analysis.
- A comparison of Lexicon-based approaches for Sentiment Analysis of microblog posts - based approach for sentiment analysis of Twitter posts, based on lexical resources such as SentiWordNet.
- On the negativity of negation
- Challenges in Sentiment Analysis
- A survey on sentiment analysis challenges - seven papers.
- Sentiment analysis on Trump's tweets using Python
- Donald Trump vs Hillary Clinton: sentiment analysis on Twitter mentions
- Does sentiment analysis work? A tidy analysis of Yelp reviews
- From tweets to polls: Linking text sentiment to public opinion time series
- Twitter mood predicts the stock market
- Forbes: How Quant Traders Use Sentiment To Get An Edge On The Market
- Sentdex: Quantifying the Qualitative
- Trump2Cash: A stock trading bot powered by Trump tweets - traded companies. A [related blog article](https://medium.com/@maxbraun/this-machine-turns-trump-tweets-into-planned-parenthood-donations-4ece8301e722#.3232hx7gx) describes a bot that turns Trump's tweets into Planned Parenthood donations.
- Lost at Sea: How Social Media is Helping Cruise Lines Attract Millennials
- Harry Plotter: Celebrating the 20 year anniversary with tidytext and the tidyverse in R
- Data Science 101: Sentiment Analysis in R Tutorial
- Cannes Lions 2017: Hungerithm, Mars Chocolate Australia (Clemenger BBDO, Melbourne)
- Sentiment analysis: 10 applications and 4 services
- What Your Boss Could Learn by Reading the Whole Company’s Emails - mails). Text analytics and NLP have become an increasingly popular approach to help search for clues that may indicate the level of employee engagement in the workplace, and any potential ‘red-flags’ that should receive particular attention by an organization and its ethical implications.
- Aspect Based Sentiment Analysis of Amazon Product Reviews
- Sentiment Analysis of 2.2 million tweets from Super Bowl 51
- Emotion and Sentiment Analysis: A Practitioner’s Guide to NLP
- Streaming Analytics Tutorial on Azure
- How to Analyze sentiment in Azure
- how-to-perform-sentiment-analysis-using-python-tutorial/
- Twitter Sentiment Analysis Overview - by-step walkthrough on how to perform sentiment analysis using TextBlob.
- ELMO embeddings in Keras using Tensorflow Hub
- Twitter Sentiment Analysis in Python using TextBlob
- Text Summarization with Gensim
- Unsupervised Text Summarization using Sentence Embeddings
- Improving Abstraction in Text Summarization
- Text Summarization and Categorization for Scientific and Health-Related Data
- Text summarization with TensorFlow
- Blog Post: Found in translation: More accurate, fluent sentences in Google Translate
- NYTimes: The Great A.I. Awakening
- Machine Learning Translation and the Google Translate Algorithm
- Neural Machine Translation (seq2seq) Tutorial
- Paper Dissected: “Attention is All You Need” Explained
- The Annotated Transformer - by-line implementation of "Attention Is All You Need".
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - research/bert). [Pytorch port.]( https://github.com/codertimo/BERT-pytorch)
- Phrase-Based & Neural Unsupervised Machine Translation - based model. Awarded as the Best Paper Award at EMNLP 2018. [Implementation code](https://github.com/facebookresearch/UnsupervisedMT).
- Meet Lucy: Creating a Chatbot Prototype
- Microsoft Bot Framework
- Training Millions of Personalized Dialogue Agents
- Ultimate Guide to Leveraging NLP & Machine Learning for your Chatbot
- Building a Simple Chatbot from Scratch in Python (using NLTK)
- A Survey on Dialogue Systems: Recent Advances and New Frontiers
- Examining the Impact of an Automated Translation Chatbot on Online Collaborative Dialog for Incidental L2 Learning
- Create a banking chatbot with FAQ discovery, anger detection and natural language understanding
- Generative Model Chatbots- May 2017
- A Guide to Building a Multi-Featured Slackbot with Python- March 2017
- Building a Simple Chatbot from Scratch in Python (Using NLTK)- September 2018
- The Road to a Conversational Banking Future-February 2019
- Chatbots - Designing intents and entities for NLP Models
- Task-oriented Dialogue System for Automatic Diagnosis
- Li Deng at AI Frontiers: Three Generations of Spoken Dialogue Systems (Bots)
- NLP — Building a Question Answering model
- agrep method in R
- fuzzywuzzy package in R
- Fuzzy String Matching – a survival skill to tackle unstructured information
- R package fastLink: Fast Probabilistic Record Linkage
- Fuzzy merge in R
- Learning Text Similarity with Siamese Recurrent Networks
- Dedupe - resolution.
- recordlinkage
- The Current Best of Universal Word Embeddings and Sentence Embeddings
- An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec
- An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation
- Document Embedding with Paragraph Vectors
- GloVe Word Embeddings Demo
- Text Classification With Word2Vec
- Document Embedding
- From Word Embeddings To Document Distances
- Word Embeddings, Bias in ML, Why You Don't Like Math, & Why AI Needs You
- Word Vectors in Natural Language Processing: Global Vectors (GloVe)
- Doc2Vec Tutorial on the Lee Dataset
- Word Embeddings in Python with SpaCy and Gensim
- Deep Contextualized Word Represenations - tf)
- Universal Language Model Fine-tuning for Text Classification
- Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
- Learned in Translation: Contextualized Word Vectors
- Distributed Representations of Sentences and Documents - technologies.com/doc2vec-tutorial/)
- sense2vec
- Skip Thought Vectors
- Sequence to Sequence Learning with Neural Networks
- The Amazing Power of Word Vectors
- Contextual String Embeddings for Sequence Labeling
- A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks - task learning approach for a set of interrelated NLP tasks. Presented at AAAI conference in January 2019.[Implementation code](https://github.com/huggingface/hmtl).
- An Idiot’s Guide to Word2vec Natural Language Processing
- Get Busy with Word Embeddings- An Introduction (February 2018)
- NLP's ImageNet moment has arrived - trained NLP language models, drawing parallels to ImageNet's contributions to computer vision.
- Word2vec: fish + music = bass
- Universal Sentence Encoder Visually Explained
- Understanding Large Language Models
- A Primer in BERTology: What we know about how BERT works
- A review of BERT based models
- BERT Explained - State of the art language model for NLP
- The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
- Machines Beat Humans on a Reading Test. But Do They Understand?
- WHAT EVERY NLP ENGINEER NEEDS TO KNOW ABOUT PRE-TRAINED LANGUAGE MODELS
- the transformer … “explained”?
- The Illustrated Transformer
- Hugging Face's course on Transformer Models
- OpenAI: Better Language Models and Their Implications - trained Transformer-based unsupervised language model that achieves state-of-the-art on many language benchmarks with focus on text generation. Controversial limited release. February 14, 2019.
- ChatGPT launch blog
- Awesome ChatGPT Prompts
- ChatGPT User Experience: Implications for Education
- New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments
- Educators Battle Plagiarism As 89% Of Students Admit To Using OpenAI’s ChatGPT For Homework
- ChatGPT: Educational friend or foe? - Pasek and Blinkoff (Temple University). January 2023.
- Don’t Ban ChatGPT in Schools. Teach With It.
- ChatGPT and the Future of Business Education
- Udemy course (January 2023). ChatGPT for Teachers in Education.
- Keras LSTM tutorial – How to easily build a powerful deep learning language model
- Deep Learning for Natural Language Processing: Tutorials with Jupyter Notebooks
- A Survey of the Usages of Deep Learning in Natural Language Processing
- Sequence Classification with Human Attention - tracking corpora to regularize attention in recurrent neural networks (RNN). [Implementation code](https://github.com/coastalcph/Sequence_classification_with_human_attention).
- Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python
- Multi-Task Deep Neural Networks for Natural Language Understanding
- Natural Language Processing Tutorial for Deep Learning Researchers
- Deep Learning for Sentiment Analysis : A Survey
- NEURAL READING COMPREHENSION AND BEYOND - Reading comprehension models built on top of deep neural networks.
- Microsoft: Multi-Task Deep Neural Network (MT-DNN)
- A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING
- Investigating Capsule Networks with Dynamic Routing for Text Classification
- Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
- TWITTER SENTIMENT ANALYSIS USING CAPSULE NETS AND GRU
- Identifying Aggression and Toxicity in Comments using Capsule Network
- Dynamic Routing Between Capsules
- MATRIX CAPSULES WITH EM ROUTING
- Using fastText and Comet.ml to classify relationships in Knowledge Graphs
- WTF is a knowledge graph?
- A survey of graphs in natural language processing
- NeurIPS
- Association for Computational Linguistics (ACL)
- Empirical Methods in Natural Language Processing (EMNLP)
- North American Chapter of the Association for Computational Linguistics (NAACL)
- European Chapter of the Association for Computational Linguistics (EACL)
- International Conference on Computational Linguistics(COLING)
- SQuAD leaderboard - performing NLP models on the Stanford Question Answering Dataset (SQuAD).
- SQuAD 1.0 paper
- SQuAD 2.0 paper
- GLUE leaderboard
- GLUE paper - sentence tasks (e.g. check if grammar is correct, sentiment analysis), similarity and paraphrase tasks (e.g. determine if two questions are equivalent), and inference tasks (e.g. determine whether a premise contradicts a hypothesis).
- Udemy: Deep Learning and NLP A-Z™: How to create a ChatBot
- Udemy: Natural Language Processing with Deep Learning in Python
- Udemy: NLP - Natural Language Processing with Python
- Udemy: Deep Learning: Advanced NLP and RNNs
- Udemy: Natural Language Processing and Text Mining Without Coding
- Stanford CS 224N / Ling 284
- Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)
- Courses for "natural language processing" on Coursera
- Coursera: Applied Text Mining in Python
- Coursera: Nartual Language Processing
- Coursera: Sequence Models for Time Series and Natural Language Processing
- Coursera: Coursera: Clinical Natural Language Processing
- DataCamp: Natural Language Processing Fundamentals in Python
- DataCamp: Sentiment Analysis in R: The Tidy Way
- DataCamp: Text Mining: Bag of Words
- DataCamp: Building Chatbots in Python
- DataCamp: Advanced NLP with spaCy
- Deep Learning Drizzle
- Natural Language Processing | Dan Jurafsky, Christopher Manning
- Deep Learning for NLP
- CMU CS 11-747: Neural Network for NLP
- YSDA NLP course
- CMU Language and Statistics II: (More) Empirical Methods in Natural Language Processing
- UT CS 388: Natural Language Processing
- Columbia: COMS W4705: Natural Language Processing
- Columbia: COMS E6998: Machine Learning for Natural Language Processing (Spring 2012)
- Machine Translation: Spring 2016
- Commonlounge: Learn Natural Language Processing: From Beginner to Expert
- Big Data University: Advanced Text Analytics – Getting Results with SystemT
- Udacity: Natural Language Processing Nanodegree
- edX: Natural Language Processing
- R packages
- tm
- lsa
- lda
- textir
- corpora
- tau
- tidytext
- Sentiment140
- sentimentr - based sentiment analysis.
- cleanNLP - based sentiment analysis.
- RSentiment - based sentiment analysis. Contains support for negation detection and sarcasm.
- text2vec - friendly tools for text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), similarities.
- fastTextR
- LDAvis
- keras - level neural networks 'API'. ([RStudio Blog: TensorFlow for R](https://blog.rstudio.com/2018/02/06/tensorflow-for-r))
- retweet - recipes/))
- topicmodels
- textmineR
- wordVectors
- gtrendsR
- Analyzing Google Trends Data in R
- textstem
- NLPutils
- Udpipe
- Python modules
- NLTK
- Video: NLTK with Python 3 for Natural Language Processing
- scikit-learn
- Tutorial
- Spark NLP - grade, scalable, and trainable versions of the latest research in natural language processing.
- spaCy - Strength Natural Language Processing in Python.
- textblob
- Natural Language Basics with TextBlob
- Gensim
- Pattern.en - of-speech tagger for English, sentiment analysis, tools for English verb conjugation and noun singularization & pluralization, and a WordNet interface.
- textmining
- Scrapy
- lda2vec
- PyText - learning based NLP modeling framework built on PyTorch.
- sent2vec
- flair - of-the-art Natural Language Processing (NLP)
- word_forms - -> "elect", "electoral", "electorate" etc.
- AllenNLP - source NLP research library, built on PyTorch.
- Beautiful Soup
- BigARTM
- Scattertext
- embeddings
- fastText
- Google Seq2Seq - purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more.
- polyglot
- textacy
- Glove-Python
- Bert As A Service - length sentence to a fixed-length vector. Design intent to provide a scalable production ready service, also allowing researchers to apply BERT quickly.
- Keras-BERT
- Paragraph embedding scripts and Pre-trained models - trained Doc2Vec and Word2Vec models
- Texthero
- Apache Tika
- Apache Spark - purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.
- MLlib
- LDA
- Word2Vec - size vector. The Word2VecModel transforms each document into a vector using the average of all words in the document
- TFIDF - inverse document frequency
- HDF5
- h5py
- Stanford CoreNLP
- Introduction to StanfordNLP: An Incredible State-of-the-Art NLP Library for 53 Languages (with Python code)
- Stanford Parser
- Stanford POS Tagger - of-Speech tagger.
- Stanford Named Entity Recognizer
- Stanford Classifier
- Stanford OpenIE
- Stanford Topic Modeling Toolbox
- MALLET
- Apache OpenNLP
- Streamcrab - Time, Twitter sentiment analyzer engine http:/www.streamcrab.com
- TextRazor API
- fastText
- Comparison of Top 6 Python NLP Libraries
- pyCaret's NLP Module - code machine learning library in Python that aims to reduce the cycle time from hypothesis to insights; also, PyCaret's Founder Moez Ali is a Smith Alumni - MMA 2020.
- Systran - Enterprise Translation Products
- SAS Text Miner (Part of SAS Enterprise Miner)
- SAS Sentiment Analysis
- STATISTICA
- Text Mining (Big Data, Unstructured Data)
- KNIME
- RapidMiner
- Gate
- IBM Watson
- Video: How IBM Watson learns (3 minutes)
- Video: IBM Watson on Jeapardy! (10 minutes)
- Video: IBM Watson: The Science Behind an Answer (7 minutes)
- Crimson Hexagon
- Stocktwits
- Meltwater
- CrowdFlower
- Lexalytics Sematria
- Rosette Text Analytics
- Alchemy API
- Monkey Learn
- LightTag Annotation Tool
- UBIAI - to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling
- Anafora - based raw text annotation tool
- brat
- Google's Colab - to-go Notebook environment that makes it easy to get up and running.
- Lyrebird.ai - Realistic Voice Cloning and Text-to-Speech” recognition platform. This Canadian start-up has created a product/platform that syncs both voice cloning with text-to-speech. Lyrebird recognizes the intonations and voice patterns from audio recordings, and overlays text data input to recreate a text-to-speech audio file output from the selected voice pattern audio recording.
- Ask Data by Tableau Software Inc. - on to help assist existing Tableau platform users with retrieving quick and easy data visualizations to drive business intelligence insights. Similar to a search engine user interface, Tableau’s Ask Data feature interface applies NLP from user text input to extract key words to find data analytics and business insights quickly on the Tableau Platform.
- Dialogflow
- Weka - to-use, graphical Machine Learning Workbench including NLP capabilities.
- Annotation Lab - Free End-to-End No-Code platform for text annotation and DL model training/tuning. Out-of-the-box support for Named Entity Recognition, Classification, Relation extraction and Assertion Status Spark NLP models. Unlimited support for users, teams, projects, documents.
- Microsoft Azure Text Analytics
- Amazon Lex
- Amazon Comprehend
- Google Cloud Natural Language
- IBM Watson
- Video: How IBM Watson learns (3 minutes)
- Video: IBM Watson on Jeapardy! (10 minutes)
- Video: IBM Watson: The Science Behind an Answer (7 minutes)
- Apache PDFBox
- Tabula: A tool for liberating data tables locked inside PDF files.
- PDFLayoutTextStripper: Converts a pdf file into a text file while keeping the layout of the original pdf.
- pdftabextract: A set of tools for extracting tables from PDF files helping to do data mining on (OCR-processed) scanned documents.
- SO: How to extract text from a PDF?
- Tools for Extracting Data and Text from PDFs - A Review
- How I used NLP (SpaCy) to screen Data Science Resumes
- PyPDF2
- MIT OpenNPT for neural machine translation and neural sequence modeling
- Stanford Parser
- Stanford CoreNLP
- word2vec demo
- Another word2vec demo
- sense2vec: Semantic Analysis of the Reddit Hivemind
- RegexPal
- AllenNLP Demo
- Cognitive Computation Group - Part of Speech Tagging Demo - of-speech tagging, information extraction tasks etc.
- UCI's Text Datasets
- data.world's Text Datasets
- Awesome Public Datasets' Natural Languge
- Insight Resources Datasets
- Bing Sentiment Analysis
- Consumer Complaint Database
- Sentiment Labelled Sentences Data Set
- Amazon product data
- Data is Plural
- FiveThirtyEight's datasets
- r/datasets
- Awesome public datasets
- R's `datasets` package
- 200,000 Russian Troll Tweets - Released by Congress from Twitter suspended accounts and removed from public view.
- Wikipedia: List of datasets for ML research
- Google Dataset Search
- Kaggle: UMICH SI650 - Sentiment Classification
- Lee's Similarity Data Sets
- Corpus of Presidential Speeches (CoPS) and a Clinton/Trump Corpus
- 15 Best Chatbot Datasets for Machine Learning
- A Survey of Available Corpora for Building Data-Driven Dialogue Systems
- nlp-datasets
- Hate-speech-and-offensive-language
- First Quora Dataset Release: Question Pairs
- The Best 25 Datasets for Natural Language Processing
- SWAG - scale dataset created for Natural Language Inference (NLI) with common-sense reasoning.
- MIMIC
- Clinical NLP Dataset Repository - available clinical datasets for use in NLP research.
- Million Song Lyrics
- The Multi-Genre NLI Corpus
- Twitter US Airline Sentiment
- Million Song Lyrics - Of-Words (BOW) format.
- DuoRC - answer pairs with evaluation script for Paraphrased Reading Comprehension
- EDGAR Financial Statements
- American National Corpus Download
- Santa Barbara Corpus of Spoken American English
- Leipzig Corpora Collection: Corpora in English, Arabic, French, Russian, German
- Awesome Twitter
- The Big Bad NLP Database
- CBC News Coronavirus articles
- Huggingface
- MPQA Lexicon
- SentiWordNet
- AFINN
- Bing
- nrc
- vaderSentiment
- AskReddit: People with a mother tongue that isn't English, what are the most annoying things about the English language when you are trying to learn it?
- Funny Video: Emotional Spell Check
- How to win Kaggle competition based on NLP task, if you are not an NLP expert
- Detecting Gang-Involved Escalation on Social Media Using Context
- Reasoning about Actions and State Changes by Injecting Commonsense Knowledge - scale corp
- The Language of Hip Hop
- Using Natural Language Processing for Automatic Detection of Plagiarism
- Probabilistic Graphical Models: Lagrangian Relaxation Algorithms for Natural Language Processing
- Human Emotion
- A Complete Exploratory Data Analysis and Visualization for Text Data
- awesome-nlp
- awesome-machine-learning
- Awesome Deep Learning for Natural Language Processing (NLP)
- Paper with Code
- Chinese NLP Tools
- Association for Computational Linguistics Papers Anthology
- Over 150 of the Best Machine Learning, NLP, and Python Tutorials I’ve Found
- 
Programming Languages
Keywords
natural-language-processing
11
machine-learning
10
nlp
9
python
7
text-mining
6
deep-learning
5
topic-modeling
4
computational-social-science
3
pdf
3
text-visualization
3
word-embeddings
3
tensorflow
3
pytorch
3
c-plus-plus
2
python-api
2
regularizer
2
bigdata
2
bigartm
2
d3
2
eda
2
exploratory-data-analysis
2
japanese-language
2
scatter-plot
2
semiotic-squares
2
sentiment
2
stylometric
2
stylometry
2
text-as-data
2
visualization
2
word2vec
2
transformer
2
bert
2
chatbot
2
dedupe
2
entity-resolution
2
python-library
2
record-linkage
2
twitter
2
artificial-intelligence-algorithms
2
artificial-neural-networks
2
bayesian-statistics
2
computer-vision
2
deep-neural-networks
2
deep-reinforcement-learning
2
explainable-ai
2
geometric-deep-learning
2
graph-neural-networks
2
medical-imaging
2
optimization
2
pattern-recognition
2