Accurately Extracting Coherent Relevant Passages Using Hidden Markov Models
In this paper, we present a principled method for accurately extracting coherent relevant passages of variable lengths using HMMs. We show that with appropriate parameter estimation, the HMM method outperforms a number of strong baseline methods on two data sets.
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| Main Authors: | , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2005
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/1257 https://ink.library.smu.edu.sg/context/sis_research/article/2256/viewcontent/p289_jiang.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | In this paper, we present a principled method for accurately extracting coherent relevant passages of variable lengths using HMMs. We show that with appropriate parameter estimation, the HMM method outperforms a number of strong baseline methods on two data sets. |
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