Learning Frames from Text with an Unsupervised Latent Variable Model

Brendan O'Connor

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arXiv · 1307.7382

Learning Frames from Text with an Unsupervised Latent Variable Model

Brendan O'Connor

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Abstract

We develop a probabilistic latent-variable model to discover semantic frames---types of events and their participants---from corpora. We present a Dirichlet-multinomial model in which frames are latent categories that explain the linking of verb-subject-object triples, given document-level sparsity. We analyze what the model learns, and compare it to FrameNet, noting it learns some novel and interesting frames. This document also contains a discussion of inference issues, including concentration parameter learning; and a small-scale error analysis of syntactic parsing accuracy.

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Brendan O'Connor. 2013-07-28. Learning Frames from Text with an Unsupervised Latent Variable Model. https://arxiv.org/abs/1307.7382

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