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
Desenvolvemos um método para extrair exemplos negativos quando apenas exemplos positivos são dados como dados supervisionados. Este método calcula a probabilidade de ocorrência de um exemplo de entrada, que deve ser considerada positiva ou negativa. Considera um exemplo de entrada que tem alta probabilidade de ocorrência, mas não aparece no conjunto de exemplos positivos como exemplo negativo. Usamos esse método para uma das tarefas importantes no processamento de linguagem natural: detecção automática de expressões japonesas com erros ortográficos. Os resultados mostraram que o método é eficaz. Neste estudo, também descrevemos dois outros métodos que desenvolvemos para a detecção de expressões com erros ortográficos: um método combinado e um método "deixando um de fora". Nas nossas experiências, descobrimos que estes métodos também são eficazes.
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Masaki MURATA, Hitoshi ISAHARA, "Automatic Detection of Mis-Spelled Japanese Expressions Using a New Method for Automatic Extraction of Negative Examples Based on Positive Examples" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 9, pp. 1416-1424, September 2002, doi: .
Abstract: We developed a method for extracting negative examples when only positive examples are given as supervised data. This method calculates the probability of occurrence of an input example, which should be judged to be positive or negative. It considers an input example that has a high probability of occurrence but does not appear in the set of positive examples as a negative example. We used this method for one of important tasks in natural language processing: automatic detection of misspelled Japanese expressions. The results showed that the method is effective. In this study, we also described two other methods we developed for the detection of misspelled expressions: a combined method and a "leaving-one-out" method. In our experiments, we found that these methods are also effective.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_9_1416/_p
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@ARTICLE{e85-d_9_1416,
author={Masaki MURATA, Hitoshi ISAHARA, },
journal={IEICE TRANSACTIONS on Information},
title={Automatic Detection of Mis-Spelled Japanese Expressions Using a New Method for Automatic Extraction of Negative Examples Based on Positive Examples},
year={2002},
volume={E85-D},
number={9},
pages={1416-1424},
abstract={We developed a method for extracting negative examples when only positive examples are given as supervised data. This method calculates the probability of occurrence of an input example, which should be judged to be positive or negative. It considers an input example that has a high probability of occurrence but does not appear in the set of positive examples as a negative example. We used this method for one of important tasks in natural language processing: automatic detection of misspelled Japanese expressions. The results showed that the method is effective. In this study, we also described two other methods we developed for the detection of misspelled expressions: a combined method and a "leaving-one-out" method. In our experiments, we found that these methods are also effective.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Automatic Detection of Mis-Spelled Japanese Expressions Using a New Method for Automatic Extraction of Negative Examples Based on Positive Examples
T2 - IEICE TRANSACTIONS on Information
SP - 1416
EP - 1424
AU - Masaki MURATA
AU - Hitoshi ISAHARA
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2002
AB - We developed a method for extracting negative examples when only positive examples are given as supervised data. This method calculates the probability of occurrence of an input example, which should be judged to be positive or negative. It considers an input example that has a high probability of occurrence but does not appear in the set of positive examples as a negative example. We used this method for one of important tasks in natural language processing: automatic detection of misspelled Japanese expressions. The results showed that the method is effective. In this study, we also described two other methods we developed for the detection of misspelled expressions: a combined method and a "leaving-one-out" method. In our experiments, we found that these methods are also effective.
ER -