Automatically Extracting Templates from Examples for NLP Tasks
Ethel Ong, Bryan Anthony Hong, Bryan Anthony Hong, 50545, Vince Nunez, 50546 · Institutional Repositories DataBase (IRDB) · 2008
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.