One of the best effective way to learn a language is having a conversation with a native speaker. However it is often very expensive way. A good alternative way is using Dialog-Based Computer Assisted Language Learning (DB-CALL) systems. The feedback quality in DB-CALL systems is very important. Therefore, to provide various expressions as feedback information, we propose a method which extracts script and their description sentence pairs from English as a Second Language (ESL) podcast web site. A linear CRFs classifier is used to find the corresponding description sentences and several features are selected according to the characteristics of the ESL text documents. The experimental results show that the performance of our system is acceptable.
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