| Linsi Xia|
(Alternative names for this author)
|Co-authors||Gentile A.L., Iria J., Naomi Yamashita, Toru Ishida, Zhang Z.|
|Authorship||Publications (2), datasets (0), tools (0)|
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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|
|Analysis on multilingual discussion for Wikipedia translation||Machine translation
Multilingual Liquid Threads
|Proceedings - 2011 2nd International Conference on Culture and Computing, Culture and Computing 2011||English||2011||In current Wikipedia translation activities, most translation tasks are performed by bilingual speakers who have high language skills and specialized knowledge of the articles. Unfortunately, compared to the large amount of Wikipedia articles, the number of such qualified translators is very small. Thus the success of Wikipedia translation activities hinges on the contributions from non-bilingual speakers. In this paper, we report on a study investigating the effects of introducing a machine translation mediated BBS that enables monolinguals to collaboratively translate Wikipedia articles using their mother tongues. From our experiment using this system, we found out that users made high use of the system and communicated actively across different languages. Furthermore, most of such multilingual discussions seemed to be successful in transferring knowledge between different languages. Such success appeared to be made possible by a distinctive communication pattern which emerged as the users tried to avoid misunderstandings from machine translation errors. These findings suggest that there is a fair chance of non-bilingual speakers being capable of effectively contributing to Wikipedia translation activities with the assistance of machine translation.||0||0|
|Semantic relatedness approach for named entity disambiguation||Communications in Computer and Information Science||English||2010||Natural Language is a mean to express and discuss about concepts, objects, events, i.e., it carries semantic contents. One of the ultimate aims of Natural Language Processing techniques is to identify the meaning of the text, providing effective ways to make a proper linkage between textual references and their referents, that is, real world objects. This work addresses the problem of giving a sense to proper names in a text, that is, automatically associating words representing Named Entities with their referents. The proposed methodology for Named Entity Disambiguation is based on Semantic Relatedness Scores obtained with a graph based model over Wikipedia. We show that, without building a Bag of Words representation of the text, but only considering named entities within the text, the proposed paradigm achieves results competitive with the state of the art on two different datasets.||0||0|