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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kami membangunkan kaedah untuk mengekstrak contoh negatif apabila hanya contoh positif diberikan sebagai data diselia. Kaedah ini mengira kebarangkalian berlakunya contoh input, yang harus dinilai sebagai positif atau negatif. Ia menganggap contoh input yang mempunyai kebarangkalian tinggi untuk berlaku tetapi tidak muncul dalam set contoh positif sebagai contoh negatif. Kami menggunakan kaedah ini untuk salah satu tugas penting dalam pemprosesan bahasa semula jadi: pengesanan automatik ungkapan Jepun yang salah eja. Keputusan menunjukkan kaedah tersebut berkesan. Dalam kajian ini, kami juga menerangkan dua kaedah lain yang kami bangunkan untuk pengesanan ungkapan yang salah eja: kaedah gabungan dan kaedah "meninggalkan satu keluar". Dalam eksperimen kami, kami mendapati bahawa kaedah ini juga berkesan.
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Salinan
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
Salinan
@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},}
Salinan
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 -