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様々な分野における対訳コーパスを用いた構文解析器の自己学習効果の検討

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本研究では、対訳コーパス、統語べース翻訳器、機械翻訳の自動評価尺度を用いて、自己学習データを選択した上で構文解析器の自己学習を行う手法を、様々な分野を対象に適用しその効果を検証する。 本手法では構文木データを新たに人手で作成する必要が無く、対訳コーパスのみを用いて構文解析器を向上させられる利点がある。実験の結果、11種類中4種類のドメインにおいて、本手法がべースラインと比較して構文解析精度を有意に向上させることが分かった。また、提案手法による性能向上が最も期待できるドメインの特徴について調査した。なお、本実験で作成したモデルは今後公開する予定である。

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様々な分野における対訳コーパスを用いた構文解析器の自己学習効果の検討

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