Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data

By Charles Sutton, Andrew McCallum, Khashayar Rohanimanesh. 2004

939 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Search-based structured prediction (2009, neutral): “[ 49 ]. An example sentence jointly labeled for these two outputs is shown in Figure 5 (under the BIO encoding).”
Learning Conditional Random Fields for Stereo (2007, background): “Other work [23] has found that loopy belief propagation can be used effectively to compute approximate marginals and from them, approximate expectations.”
Semi-Markov Conditional Random Fields for Information Extraction (2004, methodology): “, [20, 18]), and one of these methods has also been applied to NER [3].”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “…since it is not always clear how long the window of part observations should be, hidden Markov models [188], Kalman ﬁlters [232], dynamic Bayesian networks [165] and dynamic conditional random ﬁelds [212] use hidden states to capture relevant information that might be arbitrarily far in the past.”
Multitask Learning for Mental Health Conditions with Limited Social Media Data (2017, background): “as the learning implicitly leverages interactions between them (Caruana, 1993; Sutton et al., 2007; Rush et al., 2010; Collobert et al., 2011; Søgaard and Goldberg, 2016).”
Low Resource Multi-Task Sequence Tagging - Revisiting Dynamic Conditional Random Fields (2020, methodology): “As an alternative to the standard CRF, dynamic CRFs, such as the factorial CRF (Sutton et al., 2007), explicitly model dependencies between multiple sequences of labels, but have not previously been integrated with DNNs, so until now have relied on fixed text representations that cannot be improved…”
Graphical Models over Multiple Strings (2009, background): “5Notable exceptions are Sutton et al. (2004) for chunking and tagging, Sutton and McCallum (2004) for information extraction, Smith and Eisner (2008) for dependency parsing, and Cromierès and Kurohashi (2009) for alignment.”