{"links":{},"id":2006226,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02006226","sets":["6164:6165:6630:1764652100893"]},"path":["1764652100893"],"owner":"11","recid":"2006226","title":["イギリス文学テクストの影響分析に特化したLLMモデルの開発に向けて"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-12-06"},"_buckets":{"deposit":"86f3d390-3d3e-49ed-ba69-04e0b7725a0b"},"_deposit":{"id":"2006226","pid":{"type":"depid","value":"2006226","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"イギリス文学テクストの影響分析に特化したLLMモデルの開発に向けて","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"イギリス文学テクストの影響分析に特化したLLMモデルの開発に向けて","subitem_title_language":"ja"},{"subitem_title":"Toward the Development of an LLM Model Specific to Analyze the Influence of English Literary Texts","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"イギリス文学;影響;LLM;比喩;インターテクスチュアリティ","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2025-12-06","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"control_number":"2006226","item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"中央大学 国際情報学部"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Global Informatics, Chuo University","subitem_text_language":"en"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/2006226/files/IPSJ-CH2025051.pdf","label":"IPSJ-CH2025051.pdf"},"date":[{"dateType":"Available","dateValue":"2026-12-13"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CH2025051.pdf","filesize":[{"value":"551.8 KB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"24"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"c88d64b6-06db-41ff-9330-6bb76eac7b05","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2025 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"橋本,健広"}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takehiro Hashimoto","creatorNameLang":"en"}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究は，英米文学のテクスト間の影響分析に特化した大規模言語モデルの開発を試みるものである．英米文学作品から取り出した影響あるテクスト（483件）および比喩の辞書的なデータ（2449件）を利用して学習用データセットを作成し，Sentence-BERTモデルをファインチューニングした．また別に作成した影響分析の評価用データセットを用いてモデルのパフォーマンスを考察した．paraphrase-xlmrおよびparaphrase-mpnetの二つのモデルを中心に，ファインチューニング済みモデルを含む計10種類のモデルのパフォーマンスを比較した．結果として，paraphrase-mpnetを影響のデータセットのみを用いてファインチューニングしたモデルが正確性，類似性の領域でわずかながら高い数値が出た．今後はデータセットの量を増やす必要がある．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This study attempts to develop a large language model, an LLM model specific to analyze the influence of English and American literary texts. We collected 483 pairs of texts of influence and 2449 dictionary-style entries of figure of speech data, and fine-tuned Sentence-BERT models. We evaluated model performance using an evaluation dataset for literary influence that we created. We compared the performance of ten models, including fine-tuned models, primarily based on paraphrase-xlmr and paraphrase-mpnet. We found that the fine-tuned paraphrase-mpnet, which used only the influence dataset, achieved slightly higher scores in accuracy and similarity. Future work requires expanding the dataset size.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"380","bibliographic_titles":[{"bibliographic_title":"じんもんこん2025論文集"}],"bibliographicPageStart":"373","bibliographicIssueDates":{"bibliographicIssueDate":"2025-12-06","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2025","bibliographicNumberOfPages":"8"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-12-03T01:56:21.731508+00:00","updated":"2025-12-19T09:10:00.703432+00:00"}