tutorials.md

###Tutorials

#####Quick-start

• Getting Started with gensim

#####Text to Vectors

• We first need to transform text to vectors
• String to vectors tutorial
  • Create a dictionary first that maps words to ids
  • Transform the text into vectors through dictionary.doc2bow(texts)
• Corpus streaming tutorial (For very large corpuses)

#####Models and Transformation

• Models (e.g. LsiModel, Word2Vec) are built / trained from a corpus
• Transformation interface tutorial

#####TF-IDF (Model)

• Docs, Source
• tf-idf scores are normalized (sum of squares of scores = 1)

#####Phrases (Model)

• Detects words that belong in a phrase, useful for models like Word2Vec ("new", "york" -> "new york")
• Docs, Source (uses bigram detectors underneath)
• Phrases example on How I Met Your Mother

####Topic Modeling

#####LSI (Model)

• Docs, Source (very standard LSI implementation)
• How to interpret negative LSI values
• Random Projection (used as an option to speed up LSI)

#####LDA (Model)

• Docs, Source
• Example with Android issue reports, Another example, Another example

#####Topic Model Tuning

• Colouring words by topic in a document, print words in a topics
• Topic Coherence, a metric that correlates that human judgement on topic quality.
• Compare topics and documents using Jaccard, Kullback-Leibler and Hellinger similarities
• America's Next Topic Model slides -- How to choose your next topic model, presented at Pydata London 5 July 2016 by Lev Konstantinovsky
• Classification of News Articles using Topic Modeling
• LDA: pre-processing and training tips

#####Query Similarities

• Tool to get the most similar documents for LDA, LSI
• Similarity queries tutorial

#####Dynamic Topic Modeling

• Model evolution of topics through time
• Easy intro to DTM. Evolution of Voldemort topic through the 7 Harry Potter books.
• Dynamic Topic Modeling and Dynamic Influence Model Tutorial
• Python Dynamic Topic Modelling Theory and Tutorial

####Word Embeddings

#####Word2Vec (Model)

• Docs, Source (very simple interface)
• Simple word2vec tutorial (examples of most_similar, similarity, doesnt_match)
• Comparison of FastText and Word2Vec

#####Doc2Vec (Model)

• Doc2vec Quick Start on Lee Corpus
• Docs, Source (Docs are not very good)
• Doc2Vec requires a non-standard corpus (need sentiment label for each document)
• Great illustration of corpus preparation, Code (Alternative, Alternative 2)
• Doc2Vec on customer review (example)
• Doc2Vec on Airline Tweets Sentiment Analysis
• Doc2vec to predict IMDB review star rating. Reproducing the Google paper

#####Similarity Queries

• Similarity queries using Annoy with word2vec and doc2vec

#####Word Movers Distance

• Tool to get the most similar documents for word2vec
• Word Movers Distance for Yelp Reviews tutorial

#####Deep Inverse Regression

• Document Classification using Bayesian Inversion and several word2vec models(one for each class)
• Deep Inverse Regression with Yelp Reviews

####Other techniques

#####Summarization

• Extract most important keywords and sentences from the text
• Tutorial on TextRank summarisation

#####Overviews

• Tutorial showing API for document classification with various techniques: TF-IDF, word2vec averaging, Deep IR, Word Movers Distance and doc2vec
• Movie plots by genre

###Videos

• Radim Řehůřek - Faster than Google? Optimization lessons in Python.
• MLMU.cz - Radim Řehůřek - Word2vec & friends (7.1.2015)
• Making an Impact with NLP -- Pycon 2016 Tutorial by Hobsons Lane
• NLP with NLTK and Gensim -- Pycon 2016 Tutorial by Tony Ojeda, Benjamin Bengfort, Laura Lorenz from District Data Labs
• Word Embeddings for Fun and Profit -- Talk at PyData London 2016 talk by Lev Konstantinovskiy. See accompanying repo

#Credits Based on wonderful resource by Jason Xie.