Debugging, Scraping, and NLP
This month, we'll have a lightning talk from Ryan Kuhl on debugging and a full talk from Stephen McInerney on Web scraping and NLP. We hope that you can join us!
Lightning Talk: Debugging with ipdb
Speaker: Ryan Kuhl
Ryan is a Miami based software engineer at Tatari, co-founder of Public Sector ML, and student at Georgia Institute of Technology. Ryan has been programming professionally with python for 9 years and loves to build performant APIs and chunky SQL queries! When not programming for work he's studying machine learning and quantum computing. Connect to Ryan via email at firstname.lastname@example.org, LinkedIn at linkedin.com/in/kuhl or GitHub at GitHub.com/lame.
NLP, Topic Modeling and Scraping of conference talks to find which topics are hot and not
Speaker: Stephen McInerney
NLP (Natural Language Processing) and Topic Modeling are subdomains of Machine Learning which are core technologies for Python data scientists; and the automated collection of data by Scraping (in a TOS-compliant, ethical way) is a rarely-discussed practice.
- Review the basic steps, present a typical pipeline for Scraping+NLP+Topic Modeling and cover packages used As a motivating example, we investigate changes in Python conference topics 2016-2022, and statistically extract conclusions on what's hot and not, as of 2022
- We also handle foreign-language abstracts and outline how machine translation can be used for Topic Modeling
- We illustrate best practices in Scraping on text data, maximally preserving and augmenting with metadata
- Review the basic steps, present a typical pipeline (segmentation, handling Unicode, Levenshtein distance, word-vectors, Transformer, NER, IE).
- Overview of related NLP/ML/Deep Learning packages we use both for prototyping and production.
- Topic Modeling using LDA is a highly iterative clustering process to "learn" which topics seem to be similar/related/identical/different
- In this specific case, we augment conference abstracts with whatever metadata is helpful to topic-modeling e.g. speaker interests, affiliation, links to Twitter
- Example: "token" means an entirely different topic when it co-occurs with "crypto"/"blockchain"/"web3" versus when it co-occurs with "API"/"authentication"/"appsec"/"2FA"/"Oauth". But how do we automatically learn hundreds and then thousands of such cases?
Stephen is a data scientist and NLP specialist for over a decade, specializing in domain-specific (biotech/legal/financial) and multilingual NLP, in both startups and large companies. Kaggle competitor; have led "Kaggle Together" classes. Former Data Science co-chair of SF Bay Area ACM and organizer of multiple Data Science Camps. Passionate about open-source. www.linkedin.com/in/stephenmcinerney
Code of Conduct
Interactions online have less nuance than in-person interactions. Please be Open, Considerate and Respectful. Also, please refrain from discussing topics unrelated to the Python community or the technical content of the meeting.
We will conduct the meeting via Zoom. Please register in advance. To do so, go to the Meetup page for this event: https://www.meetup.com/baypiggies/events/288471326/. If you RSVP "Yes" to this event on MeetUp, the link to the Zoom meeting will be displayed.