Automatically extracting templates from examples for NLP tasks
In this paper, we present the approaches used by our NLP systems to automatically extract templates for example-based machine translation and pun generation. Our translation system is able to extract an average of 73.25% correct translation templates, resulting in a translation quality that has a lo...
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| Main Authors: | , , |
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| Format: | text |
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Animo Repository
2008
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| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/500 |
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| Institution: | De La Salle University |
| Summary: | In this paper, we present the approaches used by our NLP systems to automatically extract templates for example-based machine translation and pun generation. Our translation system is able to extract an average of 73.25% correct translation templates, resulting in a translation quality that has a low word error rate of 18% when the test document contains sentence patterns matching the training set, to a high 85% when the test document is different from the training corpus. Our pun generator is able to extract 69.2% usable templates, resulting in computer-generated puns that received an average score of 2.13 as compared to 2.7 for human-generated puns from user feedback. © 2007 by Ethel Ong, Bryan Anthony Hong, and Vince Andrew Nuñez. |
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