Interactive Information Extraction with Constrained Conditional Random Fields

By Trausti T. Kristjansson, Aron Culotta, Paul A. Viola, Andrew McCallum. 2004

166 citations recorded by the source corpus.

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Citations in the corpus

Towards Open Intent Discovery for Conversational Text (2019, neutral): “These constraints operate at the level of the fully inferred sequence, and cannot be easily integrated into the Viterbi decoding algorithm by straightforward techniques like modifying its transition matrix [27].”
Hidden Conditional Random Fields for Gesture Recognition (2006, methodology): “[10] and have been widely used since then in the natural language processing community for tasks such as noun coreference resolution [13], name entity recognition [12] and information extraction [4].”
Named Entity Recognition in the Medical Domain with Constrained CRF Models (2017, background): “The work most related to our approach for applying constraints with CRF decoding is (Culotta and McCallum, 2004; Roth and Yih, 2005; Kristjansson et al., 2004).”
Integer linear programming inference for conditional random fields (2005, methodology): “We follow the definition of the PropBank ( Kingsbury & Palmer, 2002 ) project and the CoNLL-2004 shared task definition (Carreras & M` arquez, 2004).”
A Framework and Tool for Collaborative Extraction of Reliable Information (2013, background): “…are also some works on the extraction of highly reliable information by including a human in the extraction practice by either writing regular expressions (Caruana et al., 2000), or by correcting mistakes made by automatic extraction methods (Kristjansson et al., 2004; Culotta and McCallum, 2005).”
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields (2007, background): “However, discriminative models such as conditional random fields (CRF), have recently been shown to outperform generative techniques in areas such as natural language processing [18, 37], information extraction [31, 15], web page classification [38], and computer vision [17, 33].”
Semi-Supervised Active Learning for Sequence Labeling (2009, methodology): “Another work also closely related to ours is that of Kristjansson et al. (2004). In an information extraction setting, the confidence per extracted field is calculated by a constrained variant of the ForwardBackward algorithm.”