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Research on Domain Self‐Aadaptation of Chinese‐English EBMT
Hongfei Jiang,Muyun Yang,Tiejun Zhao 한국어정보학회 2006 한국어정보학 Vol.8 No.1
The example‐based machine translation system in the specific domain can be developed in a short time with a high translation quality. Though an EBMT system can be transplanted to a new domain quickly, when faces to the need for the multi‐doamins translation, its advantage in domain adaptability will be affected. In order to solve this problem a new domain sensative EBMT translation model is proposed. Through combining the text‐classsify technique, the proposed EBMT will judge the input text and then select the most appropriate example base for the following translations. The experiments showed that this method can improve the performance of the EBMT system and meet the need for Olympicsoriented multi‐domains translations in some extents
A Hyperlink-Extended Language Model for Microblog Retrieval
Zhongyuan Han,Muyun Yang,Leilei Kong,Haoliang Qi,Sheng Li 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.6
Microblog retrieval has received much attention in recent years. In microblog retrieval, the content linked by URLs is one of the most important information of a microblog. We present a Hyperlink-extended model for microblog retrieval that combines content of microblogs and the content of embedded hyperlinks webpages using a probabilistic ranking function based on language model. Hyperlink-extended language model incorporates the users' information retrieval requirements and the microblog author’s expression needs. Using standard TREC 2011 and TREC 2012 microblog retrieval collection, various aspects of our microblog retrieval model are evaluated. Results show our model significantly outperform the art-of-the-state URL-based approaches and the best performance of TREC 2012 microblog retrieval.
A Temporal Microblog Filtering Model
Zhongyuan Han,Muyun Yang,Leilei Kong,Haoliang Qi,Sheng Li 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.1
The rapid growth in the popularity of social networking and microblogging has led to a new way of finding and broadcasting information in the recent years. The real-time microblog filtering emerges as the times require. The task of real-time microblog filtering is to decide if subsequently posted tweets are relevant to a given query which represents the special information needs. One-side feedback is one of the most difficult problems in microblog filtering. This paper focuses on exploiting the time profile of relevant microblogs to address this problem. A temporal microblog filtering based on retrieval model is proposed. Specifically, similarity threshold achieved by the language model is adjusted according to temporal burst. Evaluated on the TREC 2012 microblog real-time filtering track dataset, the experimental results show that the proposed model is significantly better than several baselines.