Learning Frames from Text with an Unsupervised Latent Variable Model

2013/07/28 by Brendan O'Connor, Brendan O’Connor, O'Connor, Brendan · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Text Readability and Simplification #Topic Modeling #cs.CL

paper · pdf · doi:10.48550/arxiv.1307.7382

21 pages; technical report for Data Analysis Project requirement, Machine Learning Department, Carnegie Mellon University

arxiv created 2013/07/28 · openalex publication_date 2013/07/28 · arxiv updated 2013/07/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression

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