# 2004 SemiMarkovCRFforIE

- (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.

**Subject Headings:** Semi-Markov Conditional Random Field, Sequence Segmentation Statistical Models, Supervised Sequence Segmentation Task.

## Notes

## Cited By

### 2010

- (Yu, 2010) ⇒ Shun-Zheng Yu. (2010). “Hidden Semi-Markov Models.” In: Artificial Intelligence Journal, 174(2). doi:10.1016/j.artint.2009.11.011
- QUOTE: As an extension to the popular hidden Markov model (HMM), a hidden semi-Markov model (HSMM) allows the underlying stochastic process to be a semi-Markov chain. Each state has variable duration and a number of observations being produced while in the state.

### 2005

- (Daume III & Marcu, 2005) ⇒ Hal Daumé, III, and Daniel Marcu. (2005). “Learning as Search Optimization: approximate large margin methods for structured prediction.” In: Proceedings of the 22nd International Conference on Machine Learning (ICML 2005) doi:10.1145/1102351.1102373

## Quotes

### Abstract

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]\bf{x}[/math] outputs a “segmentation” of [math]\bf{x}[/math], in which labels are assigned to segments (i.e., subsequences) of [math]\bf{x}[/math] rather than to individual elements [math]x_i[/math] of [math]\bf{x}[/math]. Importantly, features for semi-CRFs can measure properties of segments, and transitions within a segment can be non-Markovian. In spite of this additional power, exact learning and inference algorithms for semi-CRFs are polynomial-time — often only a small constant factor slower than conventional CRFs. In experiments on five named entity recognition problems, semi-CRFs generally outperform conventional CRFs.

### 1. Introduction

Conditional random fields (CRFs) are a recently-introduced formalism [12] for representing a conditional model Pr(y|x), where both x and y have non-trivial structure (often sequential). Here we introduce a generalization of sequential CRFs called semi-Markov conditional random fields (or semi-CRFs). Recall that semi-Markov chain models extend hidden Markov models (HMMs) by allowing each state *s _{i}* to persist for a non-unit length of time d

_{i}

*. After this time has elapsed, the system will transition to a new state*s

*s*

_{0}, which depends only on_{i}

*; however, during the “segment” of time between [math]i[/math] to [math]i[/math] +*d

_{i}

*, the behavior of the system may be non-Markovian. Semi-Markov models are fairly common in certain applications of statistics [8, 9], and are also used in reinforcement learning to model hierarchical Markov decision processes [19].*

Semi-CRFs are a conditionally trained version of semi-Markov chains. In this paper, we present inference and learning methods for semi-CRFs. We also argue that segments often have a clear intuitive meaning, and hence semi-CRFs are more natural than conventional CRFs. We focus here on named entity recognition (NER), in which a segment corresponds to an extracted entity; however, similar arguments might be made for several other tasks, such as gene-finding [11] or NP-chunking [16].

In NER, a semi-Markov formulation allows one to easily construct entity-level features (such as “entity length” and …

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Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
---|---|---|---|---|---|---|---|---|---|---|

2004 SemiMarkovCRFforIE | Sunita Sarawagi William W. Cohen Shun-Zheng Yu | Semi-Markov Conditional Random Fields for Information Extraction | Proceedings of Advances in Neural Information Processing Systems | http://books.nips.cc/papers/files/nips17/NIPS2004 0427.pdf | 2004 |

Author | Sunita Sarawagi +, William W. Cohen + and Shun-Zheng Yu + |

journal | Proceedings of Advances in Neural Information Processing Systems + |

title | Semi-Markov Conditional Random Fields for Information Extraction + |

titleUrl | http://books.nips.cc/papers/files/nips17/NIPS2004 0427.pdf + |

year | 2004 + |