ML Lunch (Oct 21, 2013): Machine Learning for the Computational Humanities





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Published on Oct 21, 2013

Speaker: David Bamman

While machine learning plays a significant role in the community of practice known as "computational social science", one area ripe for interdisciplinary work but fraught with its own challenges are the humanities, which encompass such domains as English, Literary Studies, History and Archaeology (among many others). These areas have a long history of engaging with quantitative and computational methods (pre-dating modern notions of the "digital humanities"), and offer a fascinating, complex proving ground for classic ML problems of learning and inference.

In this talk, I will discuss recent and ongoing work into two probabilistic latent variable models that fall in this domain: the first is a model for inferring character types (or "personas") in text, where a "persona" is defined as a set of mixtures over fine-grained latent lexical classes. These lexical classes capture the stereotypical actions of which a character is the agent and patient (villains "kill" and "are foiled"), as well as the attributes by which they are described (e.g., "evil"); I present results applying this model to a collection of movie plot summaries.

The second model addresses the problem of jointly inferring the identity and social rank of members of an Old Assyrian trade network from the 2nd millennium BCE, leveraging evidence in the form of local, partial ranks over observed name mentions in cuneiform tablets to learn a global rank over (latent) individuals.

For more ML Lunch talks, visit http://www.cs.cmu.edu/~learning/


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