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
지도 데이터로 긍정적인 예시만 주어지는 경우 부정적인 예시를 추출하는 방법을 개발했습니다. 이 방법은 입력 예시의 발생 확률을 계산하는데, 이는 긍정적인지 부정적인지 판단해야 합니다. 발생 확률이 높지만 긍정적 예시 집합에 나타나지 않는 입력 예시를 부정적 예시로 간주합니다. 우리는 자연어 처리의 중요한 작업 중 하나인 철자가 틀린 일본어 표현의 자동 감지에 이 방법을 사용했습니다. 결과는 이 방법이 효과적이라는 것을 보여주었습니다. 본 연구에서는 철자가 틀린 표현을 탐지하기 위해 개발한 두 가지 다른 방법인 결합 방법과 "일대일 제외" 방법도 설명했습니다. 실험에서 우리는 이러한 방법도 효과적이라는 것을 발견했습니다.
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부
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
부
@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},}
부
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 -