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.- Anthology ID:
- D18-1123
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1005–1010
- Language:
- URL:
- https://aclanthology.org/D18-1123
- DOI:
- 10.18653/v1/D18-1123
- Cite (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.
- Cite (Informal):
- PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages (Zhang et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D18-1123.pdf