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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.

- Publication
- IEICE TRANSACTIONS on Information Vol.E85-D No.9 pp.1416-1424

- Publication Date
- 2002/09/01

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- PAPER

- Category
- Natural Language Processing

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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 -