SF Scala, Andrew McCallum:: FACTORIE: A Scala Library for Machine Learning & NLP
ai.bythebay.io Nov 2025, Oakland, full-stack AI conference Scale By the Bay 2019 is held on November 13-15 in sunny Oakland, California, on the shores of Lake Merritt: https://scale.bythebay.io. Join us!
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Practitioners in natural language processing, information integration, computer vision and other areas have achieved great empirical success using graphical models with repeated, relational structure. As researchers explore increasingly complex structures, there has been growing interest in new programming languages or toolkits that make it easier to implement such models in a flexible, yet scalable way.
Our contribution to this goal is FACTORIE, a Scala library that combines (1) focus on factor graphs as a lingua franca for statistical modeling, (2) speed and scalability, with demonstrated success on problems with billions of variables and factors, and distributed
processing, (3) object-oriented definitions of random variables, factors, inference and learning methods---enabling easy modification through subclassing, as well as straightforward descent in layers of abstraction, (4) flexibility, supporting multiple modeling and
inference paradigms.
In this talk I will introduce FACTORIE, explain its basic
organizational structure, describe its modular approach to inference and learning, relate it to several other toolkits (such as GraphLab, scikit-learn, and alernative NLP toolkits), introduce its extensive natural language processing facilities, show several code examples, give a live demo, and answer your questions.
Andrew McCallum is a Professor and Director of the Information Extraction and Synthesis Laboratory in the School of Computer Science at University of Massachusetts Amherst. This summer he is a Visiting
Research Scientist at Google. He has published over 250 papers in many areas of AI, including natural language processing, machine learning, data mining and reinforcement learning, and his work has received over 35,000 citations. He obtained his PhD from University of Rochester in 1995 with Dana Ballard and a postdoctoral fellowship from CMU with Tom Mitchell and Sebastian Thrun. In the early 2000's he was Vice President of Research and Development at at WhizBang Labs, a 170-person start-up company that used machine learning for
information extraction from the Web. He is a AAAI Fellow, the recipient of the UMass Chancellor's Award for Research and Creative Activity, the UMass NSM Distinguished Research Award, the UMass Lilly Teaching Fellowship, and research awards from Google, IBM and
Microsoft. He was the General Chair for the International Conference on Machine Learning (ICML) 2012, and is president-elect of the International Machine Learning Society, as well as member of the editorial board of the Journal of Machine Learning Research. For the past ten years, McCallum has been active in research on statistical
machine learning applied to text, especially information extraction, entity resolution, semi-supervised learning, topic models, and social network analysis. Work on probabilistic programming can be found at
http://factorie.cs.umass.edu. Work on open peer review can be found at http://openreview.net. McCallum's web page is http://www.cs.umass.edu/~mccallum.