Method for improving results in an HMM-based segmentation system by incorporating external knowledge
US6965861B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 20, 2001 |
| Grant date | Nov 15, 2005 |
| Priority date | — |
| Expiry date | Feb 21, 2024 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F40/289
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A Hidden Markov model is used to segment a data sequence. To reduce the potential for error that may result from the Markov assumption, the Viterbi dynamic programming algorithm is modified to apply a multiplicative factor if a particular set of states is re-entered. As a result, structural domain knowledge is incorporated into the algorithm by expanding the state space in the dynamic programming recurrence. In a specific example of segmenting resumes, the factor is used to reward or penalize (even require or prohibit) a segmentation of the resume that results in the re-entry into a section such as Experience or Contact Information. The method may be used to impose global constraints in the processing of an input sequence or to impose constraints to local sub-sequences.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.