Abstract
To improve and enhance information retrieval on text database, there have been many approaches proposed so far, but few investigation captures context aspects of queries (of languages) directly. Here, we propose a new approach to retrieve contextual dependencies in Japanese based on latent topic model. The key idea comes from dependency structure which captures context in the database and the queries. We examine some experimental results to see the effectiveness.
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Notes
In other words, a topic does not mean human-recognizable subject such as politics or airplane but a kind of cluster putting together by some probabilistic measure.
Early draft version of this work appeared as “Context-based Query using Dependency Structures based on Latent Topic Model” in 2nd International Conference on Model and Data Engineering (MEDI2012), Poitiers, France. We have extended the comparison with several relevant investigation, revised the discussion section and some other minor changes.
word is a syntax.
One exception is any predicate should appear as a last verb.
We mean we may generate dependencies based on this probability distribution.
Here, we assume the joint probability in a form of naive Bayesian manner.
Clinton, ZeroZero, Ashita appear where the latter two words show the names of Manga.
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Acknowledgments
The authors deeply thank the reviewers of the journal and the MEDI2012 conference for their helpful comments. The authors kept feeling as if all of us had completed the joint work of this investigation.
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Shirai, M., Yanagisawa, T. & Miura, T. Context-Based Query Using Dependency Structures Based on Latent Topic Model. J Data Semant 3, 157–168 (2014). https://doi.org/10.1007/s13740-013-0031-3
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DOI: https://doi.org/10.1007/s13740-013-0031-3