TY - GEN
T1 - SciNER
T2 - 9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020
AU - Yan, Tan
AU - Huang, Heyan
AU - Mao, Xian Ling
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - There is an increasing number of scientific publications produced by the booming science community. It is very important for automatic scientific analysis to extract entities such as tasks and methods from unstructured scientific publications. At present, the span-based methods are the best way for scientific NER tasks, which usually generate a few entities by searching hundreds of candidate spans in a sentence. However, these existing methods have a few drawbacks. Firstly, the span extractor obtains more negative samples than positive samples, and thus it makes the input extremely imbalance. Secondly, the pruner has no predictive ability at the beginning of the joint training process in an end-to-end model. To tackle the above problem, in this paper, we propose a novel scientific named entity recognizing pipeline framework, called SciNER. Specifically, in the first stage, there is a pruner to filter out most illegal entities. The span extractor in the pruner performs under-sampling to balance the positive and negative samples. In the second stage, the entity recognizer is trained by the pruned spans. Extensive experiments demonstrate that SciNER outperforms state-of-the-art baselines on several datasets in both computer science and biomedical domains (Code is available at: https://github.com/ethan-yt/sciner).
AB - There is an increasing number of scientific publications produced by the booming science community. It is very important for automatic scientific analysis to extract entities such as tasks and methods from unstructured scientific publications. At present, the span-based methods are the best way for scientific NER tasks, which usually generate a few entities by searching hundreds of candidate spans in a sentence. However, these existing methods have a few drawbacks. Firstly, the span extractor obtains more negative samples than positive samples, and thus it makes the input extremely imbalance. Secondly, the pruner has no predictive ability at the beginning of the joint training process in an end-to-end model. To tackle the above problem, in this paper, we propose a novel scientific named entity recognizing pipeline framework, called SciNER. Specifically, in the first stage, there is a pruner to filter out most illegal entities. The span extractor in the pruner performs under-sampling to balance the positive and negative samples. In the second stage, the entity recognizer is trained by the pruned spans. Extensive experiments demonstrate that SciNER outperforms state-of-the-art baselines on several datasets in both computer science and biomedical domains (Code is available at: https://github.com/ethan-yt/sciner).
KW - BERT
KW - Scientific named entity recognition
KW - Span extractor
UR - http://www.scopus.com/inward/record.url?scp=85093087319&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60450-9_65
DO - 10.1007/978-3-030-60450-9_65
M3 - Conference contribution
AN - SCOPUS:85093087319
SN - 9783030604493
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 828
EP - 839
BT - Natural Language Processing and Chinese Computing - 9th CCF International Conference, NLPCC 2020, Proceedings
A2 - Zhu, Xiaodan
A2 - Zhang, Min
A2 - Hong, Yu
A2 - He, Ruifang
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 October 2020 through 18 October 2020
ER -