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
Memandangkan jumlah kertas akademik yang semakin meningkat, mencari dan memetik rujukan yang sesuai telah menjadi tugas yang tidak penting semasa penyusunan manuskrip. Mengesyorkan segelintir kertas calon kepada draf kerja boleh meringankan beban pengarang. Pendekatan konvensional terhadap pengesyoran petikan secara amnya mempertimbangkan untuk mengesyorkan satu petikan kebenaran asas daripada manuskrip input untuk konteks pertanyaan. Walau bagaimanapun, adalah perkara biasa untuk konteks tertentu disokong oleh dua atau lebih pasangan petikan bersama. Di sini, kami mencadangkan pemodelan kertas saintifik baru untuk pengesyoran petikan, iaitu Model BERT Berbilang Positif untuk Pengesyoran Petikan (MP-BERT4REC), mematuhi satu siri objektif Berbilang Positif Triplet untuk mengesyorkan berbilang petikan positif untuk konteks pertanyaan. Pendekatan yang dicadangkan mempunyai kelebihan berikut: Pertama, objektif berbilang positif yang dicadangkan adalah berkesan dalam mengesyorkan berbilang calon positif. Kedua, kami menerima pakai pengagihan hingar berdasarkan frekuensi rujukan bersama sejarah; oleh itu, MP-BERT4REC bukan sahaja berkesan dalam mengesyorkan pasangan petikan bersama frekuensi tinggi, tetapi ia juga meningkatkan prestasi mendapatkan semula petikan frekuensi rendah dengan ketara. Ketiga, strategi pensampelan konteks dinamik yang dicadangkan menangkap maksud petik makroskopik daripada manuskrip dan memperkasakan pembenaman petikan untuk bergantung kepada kandungan, yang membolehkan algoritma meningkatkan lagi prestasi. Eksperimen pengesyoran positif tunggal dan berbilang mengesahkan bahawa MP-BERT4REC memberikan peningkatan yang ketara berbanding kaedah semasa. Ia juga berkesan mendapatkan semula senarai penuh petikan bersama dan pasangan frekuensi rendah dari segi sejarah lebih baik daripada karya terdahulu.
Yang ZHANG
China Construction Bank
Qiang MA
Kyoto University
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Salinan
Yang ZHANG, Qiang MA, "MP-BERT4REC: Recommending Multiple Positive Citations for Academic Manuscripts via Content-Dependent BERT and Multi-Positive Triplet" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 11, pp. 1957-1968, November 2022, doi: 10.1587/transinf.2022EDP7034.
Abstract: Considering the rapidly increasing number of academic papers, searching for and citing appropriate references has become a nontrivial task during manuscript composition. Recommending a handful of candidate papers to a working draft could ease the burden of the authors. Conventional approaches to citation recommendation generally consider recommending one ground-truth citation from an input manuscript for a query context. However, it is common for a given context to be supported by two or more co-citation pairs. Here, we propose a novel scientific paper modelling for citation recommendations, namely Multi-Positive BERT Model for Citation Recommendation (MP-BERT4REC), complied with a series of Multi-Positive Triplet objectives to recommend multiple positive citations for a query context. The proposed approach has the following advantages: First, the proposed multi-positive objectives are effective in recommending multiple positive candidates. Second, we adopt noise distributions on the basis of historical co-citation frequencies; thus, MP-BERT4REC is not only effective in recommending high-frequency co-citation pairs, but it also significantly improves the performance of retrieving low-frequency ones. Third, the proposed dynamic context sampling strategy captures macroscopic citing intents from a manuscript and empowers the citation embeddings to be content-dependent, which allows the algorithm to further improve performance. Single and multiple positive recommendation experiments confirmed that MP-BERT4REC delivers significant improvements over current methods. It also effectively retrieves the full list of co-citations and historically low-frequency pairs better than prior works.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7034/_p
Salinan
@ARTICLE{e105-d_11_1957,
author={Yang ZHANG, Qiang MA, },
journal={IEICE TRANSACTIONS on Information},
title={MP-BERT4REC: Recommending Multiple Positive Citations for Academic Manuscripts via Content-Dependent BERT and Multi-Positive Triplet},
year={2022},
volume={E105-D},
number={11},
pages={1957-1968},
abstract={Considering the rapidly increasing number of academic papers, searching for and citing appropriate references has become a nontrivial task during manuscript composition. Recommending a handful of candidate papers to a working draft could ease the burden of the authors. Conventional approaches to citation recommendation generally consider recommending one ground-truth citation from an input manuscript for a query context. However, it is common for a given context to be supported by two or more co-citation pairs. Here, we propose a novel scientific paper modelling for citation recommendations, namely Multi-Positive BERT Model for Citation Recommendation (MP-BERT4REC), complied with a series of Multi-Positive Triplet objectives to recommend multiple positive citations for a query context. The proposed approach has the following advantages: First, the proposed multi-positive objectives are effective in recommending multiple positive candidates. Second, we adopt noise distributions on the basis of historical co-citation frequencies; thus, MP-BERT4REC is not only effective in recommending high-frequency co-citation pairs, but it also significantly improves the performance of retrieving low-frequency ones. Third, the proposed dynamic context sampling strategy captures macroscopic citing intents from a manuscript and empowers the citation embeddings to be content-dependent, which allows the algorithm to further improve performance. Single and multiple positive recommendation experiments confirmed that MP-BERT4REC delivers significant improvements over current methods. It also effectively retrieves the full list of co-citations and historically low-frequency pairs better than prior works.},
keywords={},
doi={10.1587/transinf.2022EDP7034},
ISSN={1745-1361},
month={November},}
Salinan
TY - JOUR
TI - MP-BERT4REC: Recommending Multiple Positive Citations for Academic Manuscripts via Content-Dependent BERT and Multi-Positive Triplet
T2 - IEICE TRANSACTIONS on Information
SP - 1957
EP - 1968
AU - Yang ZHANG
AU - Qiang MA
PY - 2022
DO - 10.1587/transinf.2022EDP7034
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2022
AB - Considering the rapidly increasing number of academic papers, searching for and citing appropriate references has become a nontrivial task during manuscript composition. Recommending a handful of candidate papers to a working draft could ease the burden of the authors. Conventional approaches to citation recommendation generally consider recommending one ground-truth citation from an input manuscript for a query context. However, it is common for a given context to be supported by two or more co-citation pairs. Here, we propose a novel scientific paper modelling for citation recommendations, namely Multi-Positive BERT Model for Citation Recommendation (MP-BERT4REC), complied with a series of Multi-Positive Triplet objectives to recommend multiple positive citations for a query context. The proposed approach has the following advantages: First, the proposed multi-positive objectives are effective in recommending multiple positive candidates. Second, we adopt noise distributions on the basis of historical co-citation frequencies; thus, MP-BERT4REC is not only effective in recommending high-frequency co-citation pairs, but it also significantly improves the performance of retrieving low-frequency ones. Third, the proposed dynamic context sampling strategy captures macroscopic citing intents from a manuscript and empowers the citation embeddings to be content-dependent, which allows the algorithm to further improve performance. Single and multiple positive recommendation experiments confirmed that MP-BERT4REC delivers significant improvements over current methods. It also effectively retrieves the full list of co-citations and historically low-frequency pairs better than prior works.
ER -