The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Para melhorar o desempenho instável do mecanismo de busca tradicional baseado em palavras-chave devido a ambiguidades de uma linguagem natural, como sinonímia e/ou polissemia, desenvolvemos um novo sistema avançado de recuperação de informações probabilísticas baseado em espaço DLSI (índice semântico latente diferencial). O novo método explora uma função de maior probabilidade a posteriori, fornecendo uma medida de confiabilidade na recuperação de um documento no banco de dados que tenha uma correspondência mais próxima com outro documento de uma consulta. Nosso experimento simples fornece uma evidência de apoio para a validade da teoria, que é capaz de capturar a intrincada variabilidade no uso de palavras, contribuindo para um mecanismo de busca contingente de contexto mais robusto.
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Liang CHEN, Naoyuki TOKUDA, Akira NAGAI, "Probabilistic Information Retrieval Method Based on Differential Latent Semantic Index Space" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 7, pp. 910-914, July 2001, doi: .
Abstract: To improve the unstable performance of the traditional keyword-based search engine due to ambiguities of a natural language such as synonymy and /or polysemy, we have developed a new advanced DLSI (differential latent semantic index) space based probabilistic information retrieval system. The new method exploits a most likelihood posteriori function providing a measure of reliability in retrieving a document in the database having a closest match with another document of a query. Our simple experiment gives a supporting evidence for the validity of the theory, which is capable of capturing the intricate variability in word usage contributing to a more robust context contingent search engine.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_7_910/_p
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@ARTICLE{e84-d_7_910,
author={Liang CHEN, Naoyuki TOKUDA, Akira NAGAI, },
journal={IEICE TRANSACTIONS on Information},
title={Probabilistic Information Retrieval Method Based on Differential Latent Semantic Index Space},
year={2001},
volume={E84-D},
number={7},
pages={910-914},
abstract={To improve the unstable performance of the traditional keyword-based search engine due to ambiguities of a natural language such as synonymy and /or polysemy, we have developed a new advanced DLSI (differential latent semantic index) space based probabilistic information retrieval system. The new method exploits a most likelihood posteriori function providing a measure of reliability in retrieving a document in the database having a closest match with another document of a query. Our simple experiment gives a supporting evidence for the validity of the theory, which is capable of capturing the intricate variability in word usage contributing to a more robust context contingent search engine.},
keywords={},
doi={},
ISSN={},
month={July},}
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TY - JOUR
TI - Probabilistic Information Retrieval Method Based on Differential Latent Semantic Index Space
T2 - IEICE TRANSACTIONS on Information
SP - 910
EP - 914
AU - Liang CHEN
AU - Naoyuki TOKUDA
AU - Akira NAGAI
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2001
AB - To improve the unstable performance of the traditional keyword-based search engine due to ambiguities of a natural language such as synonymy and /or polysemy, we have developed a new advanced DLSI (differential latent semantic index) space based probabilistic information retrieval system. The new method exploits a most likelihood posteriori function providing a measure of reliability in retrieving a document in the database having a closest match with another document of a query. Our simple experiment gives a supporting evidence for the validity of the theory, which is capable of capturing the intricate variability in word usage contributing to a more robust context contingent search engine.
ER -