Structured learning with constrained conditional models

By Ming-Wei Chang, Lev-Arie Ratinov, Dan Roth. 2012

140 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Model Architectures

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Citations in the corpus

Solving General Arithmetic Word Problems (2016, methodology): “In particular, we use the L + I scheme of CCMs, which predicts structured output by independently learning several simple components, combining them at inference time.”
Learning Optimal Classification Trees Using a Binary Linear Program Formulation (2019, background): “(Chang, Ratinov, and Roth 2012) propose a Constrained Conditional Model (CCM) framework to incorporate domain knowledge into a conditional model for structured learning, in the form of declarative constraints.”
Constructing classification trees using column generation (2018, background): “Chang et al. (2012) introduce a constrained conditional model framework that considers domain knowledge in a conditional model for structured learning in the form of declarative constraints.”
Auction optimization using regression trees and linear models as integer programs (2014, unknown): “ion methods in order to increase learning efﬁ- ciency. For instance, Bennett et al. [33] use linear programming for determining linear combination splits within two-class decision trees. Chang et al. [29] propose a Constrained Conditional Model (CCM) framework to incorporate domain knowledge into a ”
A Logic-Driven Framework for Consistency of Neural Models (2019, background): “Our work is also conceptually related to posterior regularization (Ganchev et al., 2010) and constrained conditional models (Chang et al., 2012), which integrate knowledge with statistical models.”
Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019, background): “Another knowledge domain that can be well represented by logic rules is linguistics [83], [84], [85], [90], [91], [112],”
Joint Reasoning for Temporal and Causal Relations (2018, methodology): “…extraction system and a causal extraction system, the proposed joint framework combines these two using a constrained conditional model (CCM) (Chang et al., 2012) framework, with an integer linear pro-a gramming (ILP) objective (Roth and Yih, 2004) that enforces declarative constraints…”
A Constrained Latent Variable Model for Coreference Resolution (2013, methodology): “…software_view/NESim MUC BCUB CEAFe AVG Dev Set Stanford 64.30 70.46 46.35 60.37 (Chang et al., 2012a) 65.75 70.25 45.30 60.43 (Martschat et al., 2012) 66.76 71.91 47.52 62.06 (Björkelund and Farkas, ) 67.12 71.18 46.84 61.71…”
A Joint Model for Extended Semantic Role Labeling (2011, methodology): “Our joint model is a Constrained Conditional Model (See Chang et al. (2011)), which allows us to build upon existing learned models using declarative constraints.”