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Journal on Data Semantics
Erschienen in: Journal on Data Semantics 3/2014

01.09.2014 | Original Article

Context-Based Query Using Dependency Structures Based on Latent Topic Model

verfasst von: Masato Shirai, Takashi Yanagisawa, Takao Miura

Erschienen in: Journal on Data Semantics | Ausgabe 3/2014

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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.
Vorheriger Artikel Integrating a Formal Development for DSLs into Meta-Modeling
Nächster Artikel Modeling CrowdSourcing Scenarios in Socially-Enabled Human Computation Applications

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Fußnoten
1
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.
 
2
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.
 
3
word is a syntax.
 
4
One exception is any predicate should appear as a last verb.
 
5
We mean we may generate dependencies based on this probability distribution.
 
6
Here, we assume the joint probability in a form of naive Bayesian manner.
 
7
Clinton, ZeroZero, Ashita appear where the latter two words show the names of Manga.
 
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Metadaten
Titel
Context-Based Query Using Dependency Structures Based on Latent Topic Model
verfasst von
Masato Shirai
Takashi Yanagisawa
Takao Miura
Publikationsdatum
01.09.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal on Data Semantics / Ausgabe 3/2014
Print ISSN: 1861-2032
Elektronische ISSN: 1861-2040
DOI
https://doi.org/10.1007/s13740-013-0031-3

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