@inproceedings{tan-etal-2024-turn,
title = "How to Turn Card Catalogs into {LLM} Fodder",
author = "Tan, Mary Ann and
Jiang, Shufan and
Sack, Harald",
editor = "S{\'e}rasset, Gilles and
Oliveira, Hugo Gon{\c{c}}alo and
Oleskeviciene, Giedre Valunaite",
booktitle = "Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.dlnld-1.5",
pages = "53--65",
abstract = "Bibliographical metadata collections describing pre-modern objects suffer from incompleteness and inaccuracies. This hampers the identification of literary works. In addition, titles often contain voluminous descriptive texts that do not adhere to contemporary title conventions. This paper explores several NLP approaches where greater textual length in titles is leveraged to enhance descriptive information.",
}
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%0 Conference Proceedings
%T How to Turn Card Catalogs into LLM Fodder
%A Tan, Mary Ann
%A Jiang, Shufan
%A Sack, Harald
%Y Sérasset, Gilles
%Y Oliveira, Hugo Gonçalo
%Y Oleskeviciene, Giedre Valunaite
%S Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F tan-etal-2024-turn
%X Bibliographical metadata collections describing pre-modern objects suffer from incompleteness and inaccuracies. This hampers the identification of literary works. In addition, titles often contain voluminous descriptive texts that do not adhere to contemporary title conventions. This paper explores several NLP approaches where greater textual length in titles is leveraged to enhance descriptive information.
%U https://aclanthology.org/2024.dlnld-1.5
%P 53-65
Markdown (Informal)
[How to Turn Card Catalogs into LLM Fodder](https://aclanthology.org/2024.dlnld-1.5) (Tan et al., DLnLD-WS 2024)
ACL
- Mary Ann Tan, Shufan Jiang, and Harald Sack. 2024. How to Turn Card Catalogs into LLM Fodder. In Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024, pages 53–65, Torino, Italia. ELRA and ICCL.