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Citation Metadata Extraction via Deep Neural Network-based Segment Sequence Labeling

Authors:

Dong An,

Liangcai Gao,

Zhuoren Jiang,

Runtao Liu,

Zhi TangAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Pages 1967 - 1970

https://doi.org/10.1145/3132847.3133074

Published: 06 November 2017 Publication History

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Abstract

Citation metadata extraction plays an important role in academic information retrieval and knowledge management. Current works on this task generally use rule-based, template-based or learning-based approaches but these methods usually either rely on handcrafted features or are limited with domains. Recently, neural networks have shown strong ability in addressing sequence labeling tasks.

In this paper, we propose a sequence labeling model for citation metadata extraction, called segment sequence labeling. Instead of inferring at word level, the input sequence is first divided into segments, and then features of the segments are computed to infer the label sequence of the segments. We first run experiments to validate the effectiveness of different parts of the model by comparing it with a CRF-based model and a neural network-based model. Experimental results show our model beats both models on most fields. Besides, our model is evaluated on public datasets UMass and Cora and has achieved significant performance improvement. Our model was trained on the data which were generated from BibTeX files collected on the Web and annotated automatically.

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Cited By

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Yuan ZChen BDai YLi YZheng HZhang R(2024)An Anchor Learning Approach for Citation Field LearningICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448007(12346-12350)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448007
Boukhers ZBouabdallah AAizawa AMandl TCarevic ZHinze AMayr PSchaer P(2022)Vision and natural language for metadata extraction from scientific PDF documentsProceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries10.1145/3529372.3533295(1-5)Online publication date: 20-Jun-2022
https://dl.acm.org/doi/10.1145/3529372.3533295
Nasar ZJaffry SMalik M(2018)Information extraction from scientific articlesScientometrics10.1007/s11192-018-2921-5117:3(1931-1990)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s11192-018-2921-5

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Published In

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 November 2017

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November 6 - 10, 2017

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CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Yuan ZChen BDai YLi YZheng HZhang R(2024)An Anchor Learning Approach for Citation Field LearningICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448007(12346-12350)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448007
Boukhers ZBouabdallah AAizawa AMandl TCarevic ZHinze AMayr PSchaer P(2022)Vision and natural language for metadata extraction from scientific PDF documentsProceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries10.1145/3529372.3533295(1-5)Online publication date: 20-Jun-2022
https://dl.acm.org/doi/10.1145/3529372.3533295
Nasar ZJaffry SMalik M(2018)Information extraction from scientific articlesScientometrics10.1007/s11192-018-2921-5117:3(1931-1990)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s11192-018-2921-5

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Improving sequence labeling with labeled clue sentences

Semi-Supervised Sequence Labeling with Self-Learned Features

Learning Context Using Segment-Level LSTM for Neural Sequence Labeling

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