Summary
Being able to understand the context of a piece of text is generally thought to be the domain of human intelligence. However, topic modeling and semantic analysis can be used to allow a computer to determine whether different messages and articles are about the same thing. This week we spoke with Radim Řehůřek about his work on GenSim, which is a Python library for performing unsupervised analysis of unstructured text and applying machine learning models to the problem of natural language understanding.
Brief Introduction
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Your hosts as usual are Tobias Macey and Chris Patti
Today we’re interviewing Radim Řehůřek about Gensim, a library for topic modeling and semantic analysis of natural language.
Interview with Radim Řehůřek
Introductions
How did you get introduced to Python? – Chris
Can you start by giving us an explanation of topic modeling and semantic analysis? – Tobias
What is Gensim and what inspired you to create it? – Tobias
What facilities does Gensim provide to simplify the work of this kind of language analysis? – Tobias
Can you describe the features that set it apart from other projects such as the NLTK or Spacy? – Tobias
What are some of the practical applications that Gensim can be used for? – Tobias
One of the features that stuck out to me is the fact that Gensim can process corpora on disk that would be too large to fit into memory. Can you explain some of the algorithmic work that was necessary to allow for this streaming process to be possible? – Tobias
Given that it can handle streams of data, could it also be used in the context of something like Spark? – Tobias
Gensim also supports unsupervised model building. What kinds of limitations does this have and when would you need a human in the loop? – Tobias
Once a model has been trained, how does it get saved and reloaded for subsequent use? – Tobias
What are some of the more unorthodox or interesting uses people have put Gensim to that you’ve heard about? – Chris
In addition to your work on Gensim, and partly due to its popularity, you have started a consultancy for customers who are interested in improving their data analysis capabilities. How does that feed back into Gensim? – Tobias
Are there any improvements in Gensim or other libraries that you have made available as a result of issues that have come up during client engagements? – Tobias
Is it difficult to find contributors to Gensim because of its advanced nature? – Tobias
Are there any resources you’d like to recommend our listeners explore to get a more in depth understanding of topic modeling and related techniques? – Chris
Keep In Touch
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Picks
Tobias
Dark Matter and the Dinosaurs by Lisa Randall
Chris
m-cli
Radim
1177 BC: The Year Civilization Collapsed
Links
Nadia Eghbal
Gensim
SQL Addict
NLTK
Spacy
Latent Dirichlet Allocation (LDA)
LSI
Keynote in Italy on distributed processing
Google Scholar references for Gensim
Stylometric analysis
On Writing Well
Student Incubator
Wikipedia on topic modeling
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA