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        <title>PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages</title>
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    <name type="personal">
        <namePart type="given">Yiqing</namePart>
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        <namePart type="given">Jianzhong</namePart>
        <namePart type="family">Qi</namePart>
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    <name type="personal">
        <namePart type="given">Chuandong</namePart>
        <namePart type="family">Yin</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Publication information in a researcher’s academic homepage provides insights about the researcher’s expertise, research interests, and collaboration networks. We aim to extract all the publication strings from a given academic homepage. This is a challenging task because the publication strings in different academic homepages may be located at different positions with different structures. To capture the positional and structural diversity, we propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF. We further propose an alternating training method for training the model. Experiments on real data show that PubSE outperforms the state-of-the-art models by up to 11.8% in F1-score.</abstract>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages
%A Zhang, Yiqing
%A Qi, Jianzhong
%A Zhang, Rui
%A Yin, Chuandong
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zhang-etal-2018-pubse
%X Publication information in a researcher’s academic homepage provides insights about the researcher’s expertise, research interests, and collaboration networks. We aim to extract all the publication strings from a given academic homepage. This is a challenging task because the publication strings in different academic homepages may be located at different positions with different structures. To capture the positional and structural diversity, we propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF. We further propose an alternating training method for training the model. Experiments on real data show that PubSE outperforms the state-of-the-art models by up to 11.8% in F1-score.
%R 10.18653/v1/D18-1123
%U https://aclanthology.org/D18-1123
%U https://doi.org/10.18653/v1/D18-1123
%P 1005-1010

Markdown (Informal)

[PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages](https://aclanthology.org/D18-1123) (Zhang et al., EMNLP 2018)

• PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages (Zhang et al., EMNLP 2018)

ACL

• Yiqing Zhang, Jianzhong Qi, Rui Zhang, and Chuandong Yin. 2018. PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1005–1010, Brussels, Belgium. Association for Computational Linguistics.