@inproceedings{lin-etal-2022-pa,
title = "{PA} Ph{\&}Tech at {S}em{E}val-2022 Task 11: {NER} Task with Ensemble Embedding from Reinforcement Learning",
author = "Lin, Qizhi and
Hou, Changyu and
Wang, Xiaopeng and
Wang, Jun and
Qiao, Yixuan and
Jiang, Peng and
Jiang, Xiandi and
Wang, Benqi and
Xiao, Qifeng",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.198",
doi = "10.18653/v1/2022.semeval-1.198",
pages = "1444--1447",
abstract = "From pretrained contextual embedding to document-level embedding, the selection and construction of embedding have drawn more and more attention in the NER domain in recent research. This paper aims to discuss the performance of ensemble embeddings on complex NER tasks. Enlightened by Wang{'}s methodology, we try to replicate the dominating power of ensemble models with reinforcement learning optimizor on plain NER tasks to complex ones. Based on the composition of semeval dataset, the performance of the applied model is tested on lower-context, QA, and search query scenarios together with its zero-shot learning ability. Results show that with abundant training data, the model can achieve similar performance on lower-context cases compared to plain NER cases, but can barely transfer the performance to other scenarios in the test phase.",
}
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%0 Conference Proceedings
%T PA Ph&Tech at SemEval-2022 Task 11: NER Task with Ensemble Embedding from Reinforcement Learning
%A Lin, Qizhi
%A Hou, Changyu
%A Wang, Xiaopeng
%A Wang, Jun
%A Qiao, Yixuan
%A Jiang, Peng
%A Jiang, Xiandi
%A Wang, Benqi
%A Xiao, Qifeng
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F lin-etal-2022-pa
%X From pretrained contextual embedding to document-level embedding, the selection and construction of embedding have drawn more and more attention in the NER domain in recent research. This paper aims to discuss the performance of ensemble embeddings on complex NER tasks. Enlightened by Wang’s methodology, we try to replicate the dominating power of ensemble models with reinforcement learning optimizor on plain NER tasks to complex ones. Based on the composition of semeval dataset, the performance of the applied model is tested on lower-context, QA, and search query scenarios together with its zero-shot learning ability. Results show that with abundant training data, the model can achieve similar performance on lower-context cases compared to plain NER cases, but can barely transfer the performance to other scenarios in the test phase.
%R 10.18653/v1/2022.semeval-1.198
%U https://aclanthology.org/2022.semeval-1.198
%U https://doi.org/10.18653/v1/2022.semeval-1.198
%P 1444-1447
Markdown (Informal)
[PA Ph&Tech at SemEval-2022 Task 11: NER Task with Ensemble Embedding from Reinforcement Learning](https://aclanthology.org/2022.semeval-1.198) (Lin et al., SemEval 2022)
ACL
- Qizhi Lin, Changyu Hou, Xiaopeng Wang, Jun Wang, Yixuan Qiao, Peng Jiang, Xiandi Jiang, Benqi Wang, and Qifeng Xiao. 2022. PA Ph&Tech at SemEval-2022 Task 11: NER Task with Ensemble Embedding from Reinforcement Learning. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1444–1447, Seattle, United States. Association for Computational Linguistics.