Byung-kwan Kwak

Named entity tagging for korean using DL-CoTrain algorithm

Our approach to solve the problem of Korean named entity classification adopted a co-training method called DL-CoTrain. We use only a part-of-speech tagger and a simple noun phrase chunker instead of a full parser to extract the contextual features of a named entity. We will discuss the linguistic features in Korean which are valuable for named entity classification and experimentally show how large a labeled corpus and which unlabeled corpus is necessary for the better performance and portability of a named entity classifier. With only about a quarter of the labeled corpus, our method can compete with its supervised counterpart.

Fast multimedia contents retrieval by partially spoken query

We present novel fast multi-pass decoding strategies for recognizing large named-entities on a low-resource embedded device and thus retrieving MP3 music using spoken query, which contains partial segments of whole music titles and artists. After acoustic-phonetic decoding in the first stage processing, we incorporate word boundary information with phonetic confusion matrix into next stage partial word matching. Then, we rescore candidate phone lists using more complex context-dependent acoustic model, whose outputs are the retrieved songs. We tested our retrieval system to the task of retrieving 1000 songs on a commercial MP3 player and could achieve about 15.5% relative improvements in response time over conventional frame-based multi-pass decoding method without sacrificing recognition rates.