Masahiro Tanaka


WISDOM X, DISAANA and D-SUMM: Large-scale NLP Systems for Analyzing Textual Big Data
Junta Mizuno | Masahiro Tanaka | Kiyonori Ohtake | Jong-Hoon Oh | Julien Kloetzer | Chikara Hashimoto | Kentaro Torisawa
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

We demonstrate our large-scale NLP systems: WISDOM X, DISAANA, and D-SUMM. WISDOM X provides numerous possible answers including unpredictable ones to widely diverse natural language questions to provide deep insights about a broad range of issues. DISAANA and D-SUMM enable us to assess the damage caused by large-scale disasters in real time using Twitter as an information source.


WISDOM2013: A Large-scale Web Information Analysis System
Masahiro Tanaka | Stijn De Saeger | Kiyonori Ohtake | Chikara Hashimoto | Makoto Hijiya | Hideaki Fujii | Kentaro Torisawa
The Companion Volume of the Proceedings of IJCNLP 2013: System Demonstrations


Open-Source Platform for Language Service Sharing
Yohei Murakami | Masahiro Tanaka | Donghui Lin | Toru Ishida
Proceedings of the Workshop on Language Resources, Technology and Services in the Sharing Paradigm


Composing Human and Machine Translation Services: Language Grid for Improving Localization Processes
Donghui Lin | Yoshiaki Murakami | Toru Ishida | Yohei Murakami | Masahiro Tanaka
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

With the development of the Internet environments, more and more language services become accessible for common people. However, the gap between human translators and machine translators remains huge especially for the domain of localization processes that requires high translation quality. Although efforts of combining human and machine translators for supporting multilingual communication have been reported in previous research, how to apply such approaches for improving localization processes are rarely discussed. In this paper, we aim at improving localization processes by composing human and machine translation services based on the Language Grid, which is a language service platform that we have developed. Further, we conduct experiments to compare the translation quality and translation cost using several translation processes, including absolute machine translation processes, absolute human translation processes and translation processes by human and machine translation services. The experiment results show that composing monolingual roles and dictionary services improves the translation quality of machine translators, and that collaboration of human and machine translators is possible to reduce the cost comparing with the absolute bilingual human translation. We also discuss the generality of the experimental results and further challenging issues of the proposed localization processes.

Language Service Management with the Language Grid
Yohei Murakami | Donghui Lin | Masahiro Tanaka | Takao Nakaguchi | Toru Ishida
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

As the number of language resources accessible on the Internet increases, many efforts have been made for combining language resources and language processing tools to create new services. However, existing language resource coordination frameworks cannot manage issues of intellectual property associated with language resources, which make it difficult for most end-users to get supports for their intercultural collaborations because they always have to deal with the issues by themselves. In this paper, we aim at constructing a new language service management architecture on the Language Grid, which enables language resource providers to control access to their resources in accordance with their own policies. Furthermore, we apply the proposed architecture to the operating Language Grid in order to validate the effectiveness of the architecture. As a result, several service management models utilizing the monitoring and access constraints are occurring to satisfy various requirements from language resource providers. These models can handle paid-for language resources as well as free language resources. Finally, we discuss further challenging issues of combining language resources under each different policies.