Presentation | 1999/7/22 Named Entity Extraction Using Error-Driven Learning of Finite-State Transducers Manabu Sassano, Koji Tsukamoto, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This papaer describes a method of extracting named entities from Japanese text based on Eric Brill's transformation-based error-driven learning. We developed an extraction system which uses a morphological analyzer and machine-learned finite-state transducers (FSTs), and performed an experiment against the formal run (general topics) of the IREX (Information Retrieval and Extraction Exercise) NE) (named entity task). Our system learned 1,428 FSTs from the CRL NE data containing about 10,000 sentences and achieved an overall named entities F-measure of 71.28. The score was lower than that of the hand-crafted FSTs. However, the machine-learned FSTs outperformed the half of the systems participating in the IREX NE. Also, we didn't encounter overfitting in the learning process. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Information extraction / named entity task / error-driven learning / finite-state transducer |
Paper # | NLC99-6 |
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Conference Information | |
Committee | NLC |
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Conference Date | 1999/7/22(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Natural Language Understanding and Models of Communication (NLC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Named Entity Extraction Using Error-Driven Learning of Finite-State Transducers |
Sub Title (in English) | |
Keyword(1) | Information extraction |
Keyword(2) | named entity task |
Keyword(3) | error-driven learning |
Keyword(4) | finite-state transducer |
1st Author's Name | Manabu Sassano |
1st Author's Affiliation | Fujitsu Laboratories, Ltd.() |
2nd Author's Name | Koji Tsukamoto |
2nd Author's Affiliation | Fujitsu Laboratories, Ltd. |
Date | 1999/7/22 |
Paper # | NLC99-6 |
Volume (vol) | vol.99 |
Number (no) | 227 |
Page | pp.pp.- |
#Pages | 8 |
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