Now showing items 1-5 of 5
Generative Non-Markov Models for Information Extraction
(Johns Hopkins University, 2016-02-22)
Learning from unlabeled data is a long-standing challenge in machine learning. A principled solution involves modeling the full joint distribution over inputs and the latent structure of interest, and imputing the missing ...
Report Linking: Information Extraction for Building Topical Knowledge Bases
(Johns Hopkins University, 2017-10-26)
Human language artifacts represent a plentiful source of rich, unstructured information created by reporters, scientists, and analysts. In this thesis we provide approaches for adding structure: extracting and linking ...
Decompositional Semantics for Events, Participants, and Scripts in Text
(Johns Hopkins University, 2019-10-23)
This thesis presents a sequence of practical and conceptual developments in decompositional meaning representations for events, participants, and scripts in text under the framework of Universal Decompositional Semantics ...
Graphical Models with Structured Factors, Neural Factors, and Approximation-aware Training
(Johns Hopkins University, 2015-10-23)
This thesis broadens the space of rich yet practical models for structured prediction. We introduce a general framework for modeling with four ingredients: (1) latent variables, (2) structural constraints, (3) learned ...
Topic Modeling with Structured Priors for Text-Driven Science
(Johns Hopkins University, 2015-07-24)
Many scientific disciplines are being revolutionized by the explosion of public data on the web and social media, particularly in health and social sciences. For instance, by analyzing social media messages, we can instantly ...