Hiroaki Kitano


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Connecting Text Mining and Pathways using the PathText Resource
Rune Sætre | Brian Kemper | Kanae Oda | Naoaki Okazaki | Yukiko Matsuoka | Norihiro Kikuchi | Hiroaki Kitano | Yoshimasa Tsuruoka | Sophia Ananiadou | Jun’ichi Tsujii
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Many systems have been developed in the past few years to assist researchers in the discovery of knowledge published as English text, for example in the PubMed database. At the same time, higher level collective knowledge is often published using a graphical notation representing all the entities in a pathway and their interactions. We believe that these pathway visualizations could serve as an effective user interface for knowledge discovery if they can be linked to the text in publications. Since the graphical elements in a Pathway are of a very different nature than their corresponding descriptions in English text, we developed a prototype system called PathText. The goal of PathText is to serve as a bridge between these two different representations. In this paper, we first describe the overall architecture and the interfaces of the PathText system, and then provide some details about the core Text Mining components.


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Semantic Network Array Processor as a Massively Parallel Computing Platform for High Performance and Large-Scale Natural Language Processing
Hiroaki Kitano | Dan Moldovan
COLING 1992 Volume 2: The 14th International Conference on Computational Linguistics


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Toward a Plan-Based Understanding Model for Mixed-Initiative Dialogues
Hiroaki Kitano | Carol Van Ess-Dykema
29th Annual Meeting of the Association for Computational Linguistics

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Toward High Performance Machine Translation: Preliminary Results from Massively Parallel Memory-Based Translation on SNAP
Hiroaki Kitano | Dan Moldovan | Seungho Cha
Proceedings of Machine Translation Summit III: Papers

This paper describes a memory-based machine translation system developed for the Semantic Net- work Array Processor (SNAP). The goal of our work is to develop a scalable and high-performance memory-based machine translation system which utilizes the high degree of parallelism provided by the SNAP machine. We have implemented an experimental machine translation system DMSNAP as a central part of a real-time speech-to-speech dia- logue translation system. It is a SNAP version of the ΦDMDIALOG speech-to-speech translation system. Memory-based natural language processing and syntactic constraint network model has been incorporated using parallel marker-passing which is directly supported from hardware level. Experimental results demonstrate that the parsing of a sentence is done in the order of milliseconds.

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Unification Algorithms for Massively Parallel Computers
Hiroaki Kitano
Proceedings of the Second International Workshop on Parsing Technologies

This paper describes unification algorithms for fine-grained massively parallel computers. The algorithms are based on a parallel marker-passing scheme. The marker-passing scheme in our algorithms carry only bit-vectors, address pointers and values. Because of their simplicity, our algorithms can be implemented on various architectures of massively parallel machines without loosing the inherent benefits of parallel computation. Also, we describe two augmentations of unification algorithms such as multiple unification and fuzzy unification. Experimental results indicate that our algorithm attaines more than 500 unification per seconds (for DAGs of average depth of 4) and has a linear time-complexity. This leads to possible implementations of massively parallel natural language parsing with full linguistic analysis.


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Incremental Sentence Production with a Parallel Marker-Passing Algorithm
Hiroaki Kitano
COLING 1990 Volume 2: Papers presented to the 13th International Conference on Computational Linguistics


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Ambiguity Resolution in the DMTRANS PLUS
Hiroaki Kitano | Hideto Tomabechi | Lori Levin
Fourth Conference of the European Chapter of the Association for Computational Linguistics

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Massively Parallel Parsing in 𝛷DmDialog: Integrated Architecture for Parsing Speech Inputs
Hiroaki Kitano | Teruko Mitamura | Masaru Tomita
Proceedings of the First International Workshop on Parsing Technologies

This paper describes the parsing scheme in the 𝛷DmDialog speech-to-speech dialog translation system, with special emphasis on the integration of speech and natural language processing. We propose an integrated architecture for parsing speech inputs based on a parallel marker-passing scheme and attaining dynamic participation of knowledge from the phonological-level to the discourse-level. At the phonological level, we employ a stochastic model using a transition matrix and a confusion matrix and markers which carry a probability measure. At a higher level, syntactic/semantic and discourse processing, we integrate a case-based and constraint-based scheme in a consistent manner so that a priori probability and constraints, which reflect linguistic and discourse factors, are provided to the phonological level of processing. A probability/cost-based scheme in our model enables ambiguity resolution at various levels using one uniform principle.