| Eiichiro Sumita|
(Alternative names for this author)
|Co-authors||Andrew Finch, Kotaro Nakayama, Maike Erdmann, Shojiro Nishio, Takahiro Hara, Yasuda K.|
|Authorship||Publications (2), datasets (0), tools (0)|
|Citations||Total (1), average (0.5), median (0.5), max (1), min (0)|
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Eiichiro Sumita is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Calculating Wikipedia article similarity using machine translation evaluation metrics||Bilingual dictionary
Cross-language Document Similarity
|Proceedings - 25th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2011||English||2011||Calculating the similarity of Wikipedia articles in different languages is helpful for bilingual dictionary construction and various other research areas. However, standard methods for document similarity calculation are usually very simple. Therefore, we describe an approach of translating one Wikipedia article into the language of the other article, and then calculating article similarity with standard machine translation evaluation metrics. An experiment revealed that our approach is effective for identifying Wikipedia articles in different languages that are covering the same concept.||0||0|
|Method for building sentence-aligned corpus from wikipedia||AAAI Workshop - Technical Report||English||2008||We propose the framework of a Machine Translation (MT) bootstrapping method by using multilingual Wikipedia articles. This novel method can simultaneously generate a statistical machine translation (SMT) and a sentence-aligned corpus. In this study, we perform two types of experiments. The aim of the first type of experiments is to verify the sentence alignment performance by comparing the proposed method with a conventional sentence alignment approach. For the first type of experiments, we use JENAAD, which is a sentence-aligned corpus built by the conventional sentence alignment method. The second type of experiments uses actual English and Japanese Wikipedia articles for sentence alignment. The result of the first type of experiments shows that the performance of the proposed method is comparable to that of the conventional sentence alignment method. Additionally, the second type of experiments shows that wc can obtain the English translation of 10% of Japanese sentences while maintaining high alignment quality (rank-A ratio of over 0.8). Copyright||0||1|