Conditional Random Field Model Instance
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A conditional random field model instance is an undirected conditional probability network \(\boldsymbol{P}(Y|X)\) that abides by a conditional random fields metamodel.
- AKA: CRF, Conditional Random Field, Conditional Random Field Graph, CRF-based Model.
- Context:
- It represents a (set of) conditional distribution p(y|x) such that for any fixed x and factor graph \(G\) over \(Y\), p(y|x) factorizes according to G.
- Every conditional distribution p(y|x) is a CRF for some, perhaps trivial, factor graph.
- It can be produced/used by a CRF-based Learning System (that implements a CRF Training Algorithm and CRF Inference Algorithm)
- It can be a CRF-based Sequence Tagging Function/CRF-based Tagger (a Finite-State Sequence Tagging Model for a Sequence Labeling Task)
- It can be:
- It can (typically) be a Trained Conditional Random Field (Trained CRF).
- It can range from being a Small CRF to being a Medium CRF, to being a Large CRF.
- It can use:
- It can be applied to:
- Supervised Part-of-Speech Tagging (Lafferty & al, 2001).
- Supervised High-level Activity Extraction (Liao & al, 2007).
- Supervised Image Segmentation (Reynolds & Murphy, 2007); (Verbeek & Triggs, 2007).
- Supervised Object Recognition (Quattoni & al, 2004).
- Supervised Named Entity Recognition, (Sutton & McCallum, 2007); Stanford Named Entity Recognizer System.
- Supervised Concept Mention Identification, (Melli, 2010b).
- Example(s):
- the CRF Model developed in (Melli, 2010).
- Counter-Example(s):
- See: Label Bias; Joint Probability; CRF Modeling Task; CRF Inferencing Task.
References
2007
- (Sutton & McCallum, 2007) ⇒ Charles Sutton, and Andrew McCallum. (2007). "An Introduction to Conditional Random Fields for Relational Learning." In: (Getoor & Taskar, 2007).
- QUOTE: Let \(G\) be a factor graph over \(Y\) . Then \(p(y|x)\) is a conditional random field if for any fixed \(x\), the distribution \(\it{p}(\bf{y}|\bf{x})\) factorizes according to \(\it{G}\). Thus, every conditional distribution \(\it{p}(\bf{y} \vert \bf{x})\) is a CRF for some, perhaps trivial, factor graph.
... We will occasionally use the term random field to refer to a particular distribution among those defined by an undirected model. To reiterate, we will consistently use the term model to refer to a family of distributions, and random field (or more commonly, distribution) to refer to a single one.
- QUOTE: Let \(G\) be a factor graph over \(Y\) . Then \(p(y|x)\) is a conditional random field if for any fixed \(x\), the distribution \(\it{p}(\bf{y}|\bf{x})\) factorizes according to \(\it{G}\). Thus, every conditional distribution \(\it{p}(\bf{y} \vert \bf{x})\) is a CRF for some, perhaps trivial, factor graph.
- (Liao & al, 2007) ⇒ L. Liao, D. Fox, and H. Kautz. (2007). "Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields." In: International Journal of Robotics Research.
- (Verbeek & Triggs, 2007) ⇒ J. Verbeek and B. Triggs. (2007). "Scene Segmentation with Conditional Random Fields Learned from Partially Labeled Examples." In: Proceedings of Neural Information Processing Systems.
2006
- (McCallum, 2006) ⇒ Andrew McCallum. (2006). "Information Extraction, Data Mining and Joint Inference." Invited Talk at SIGKDD Conference (KDD 2006).
- QUOTE: First explorations with these models centered on finite state models, represented as linear-chain graphical models, with joint probability distribution over state sequence Y calculated as a normalized product over potentials on cliques of the graph. As is often traditional in NLP and other application areas, these potentials are defined to be log-linear combination of weights on features of the clique values. The chief excitement from an application point of view is the ability to use rich and arbitrary features of the input without complicating inference.
- CRFs have achieved state-of-the-art results in
- Noun phrase, Named entity HLT 2003, McCallum & Li @ CoNLL’03
- Protein structure prediction ICML 2004
- IE from Bioinformatics text Bioinformatics 2004
- Asian word segmentation COLING 2004, ACL 2004
- IE from Research papers HTL 2004
- Object classification in images CVPR 2004
- QUOTE: First explorations with these models centered on finite state models, represented as linear-chain graphical models, with joint probability distribution over state sequence Y calculated as a normalized product over potentials on cliques of the graph. As is often traditional in NLP and other application areas, these potentials are defined to be log-linear combination of weights on features of the clique values. The chief excitement from an application point of view is the ability to use rich and arbitrary features of the input without complicating inference.
2004
- (Quattoni & al, 2004) ⇒ Ariadna Quattoni, Michael Collins, and Trevor Darrel. (2004). "Conditional Random Fields for Object Recognition." In: Proceedings of Neural Information Processing Systems (NIPS 2004).
2001
- (Lafferty & al, 2001) ⇒ John D. Lafferty, Andrew McCallum, and Fernando Pereira. (2001). "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data." In: Proceedings of ICML Conference (ICML 2001).
