Semi-Markov Conditional Random Field

A Semi-Markov Conditional Random Field is a Conditional Random Field that ...

• AKA: Semi-Markov CRF, Markov Renewal Process.
• See: Markov Model, Semi-CRF, Hidden Semi-Markov Process.

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References

2015

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Markov_renewal_process Retrieved:2015-6-17.
  • In probability and statistics a Markov renewal process is a random process that generalizes the notion of Markov jump processes. Other random processes like Markov chain, Poisson process, and renewal process can be derived as a special case of an MRP (Markov renewal process).

2006

• (Okanohara et al., 2006) ⇒ Daisuke Okanohara, Yusuke Miyao, Yoshimasa Tsuruokam, and Jun'ichi Tsujii. (2006). “Improving the Scalability of Semi-Markov Conditional Random Fields for Named Entity Recognition.” In: Proceedings of ACL Conference (ACL 2006).

2004a

• (Sarawagi & Cohen, 2004) ⇒ Sunita Sarawagi, and William W. Cohen. (2004). “Semi-Markov Conditional Random Fields for Information Extraction.” In: Proceedings of Advances in Neural Information Processing Systems, 17 NIPS 2004.
  • QUOTE: We describe semi-Markov conditional random-fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively, a semi-CRF on an Input sequence [math]\displaystyle{ \bf{x} }[/math] outputs a “segmentation” of [math]\displaystyle{ \bf{x} }[/math], in which labels are assigned to segments (i.e., subsequences) of [math]\displaystyle{ \bf{x} }[/math] rather than to individual elements [math]\displaystyle{ x_i }[/math] of [math]\displaystyle{ \bf{x} }[/math]. Importantly, features for semi-CRFs can measure properties of segments, and transitions within a segment can be non-Markovian. ...

2004b

• (Cohen & Sarawagi, 2004) ⇒ William W. Cohen, Sunita Sarawagi. (2004). “Exploiting Dictionaries in Named Entity Extraction: Combining semi-Markov extraction processes and data integration methods.” In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004) doi:10.1145/1014052.1014065

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