Euisok Chung

GenieTutor: A Computer-Assisted Second-Language Learning System Based on Spoken Language Understanding

This paper introduces a computer-assisted second-language learning system using spoken language understanding. The system consists of automatic speech recognition, semantic/grammar correction evaluation, and tutoring module. The speech recognition is optimized for non-natives as well as natives for educational purpose and smooth interaction. Semantic/grammar correction evaluation evaluates whether the non-native learners utterance is appropriate semantically and is correct grammatically. Tutoring module decides to go to the next turn or ask the learner to try again, and also provides a turn-by-turn corrective feedback using evaluation results. We constructed English learning service consisting of three stages such as Pronunciation Clinic, Think&Talk and Look&Talk using the system.

Korean Named Entity Recognition using HMM and CoTraining Model

Named entity recognition is important in sophisticated information service system such as Question Answering and Text Mining since most of the answer type and text mining unit depend on the named entity type. Therefore we focus on named entity recognition model in Korean. Korean named entity recognition is difficult since each word of named entity has not specific features such as the capitalizing feature of English. It has high dependence on the large amounts of hand-labeled data and the named entity dictionary, even though these are tedious and expensive to create. In this paper, we devise HMM based named entity recognizer to consider various context models. Furthermore, we consider weakly supervised learning technique, CoTraining, to combine labeled data and unlabeled data.

Domain-Adapted Word Segmentation for an Out-of-Domain Language Modeling

This paper introduces a domain-adapted word segmentation approach to text where a word delimiter is not used regularly. It depends on an unknown word extraction technique. This approach is essential for language modeling to adapt to new domains since a vocabulary set is activated in a word segmentation step. We have achieved ERR 21.22% in Korean word segmentation. In addition, we show that an incremental domain adaptation of the word segmentation decreases the perplexity of input text gradually. It means that our approach supports an out-of-domain language modeling.