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.
CITATION STYLE
Zhang, Y., Qi, J., Zhang, R., & Yin, C. (2018). PubSE: A hierarchical model for publication extraction from academic homepages. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 (pp. 1005–1010). Association for Computational Linguistics. https://doi.org/10.18653/v1/d18-1123
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