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    <titleInfo>
        <title>Application of Entity Classification Model Based on Different Position Embedding in Chinese Frame Semantic Parsing</title>
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    <name type="personal">
        <namePart type="given">Zhou</namePart>
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        <namePart type="family">Sujie</namePart>
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        <namePart type="family">Junbo</namePart>
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    <name type="personal">
        <namePart type="given">Yuan</namePart>
        <namePart type="family">Xiao</namePart>
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            <title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)</title>
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            <namePart type="given">Hongfei</namePart>
            <namePart type="family">Lin</namePart>
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            <namePart type="given">Bin</namePart>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <abstract>“This paper addresses three subtasks of Chinese Frame Semantic Parsing based on the BERT and RoBERTa pre-trained models: Frame Identification, Argument Identification, and Role Identification. In the Frame Identification task, we utilize the BERT PLM with Rotary Positional Encoding for the semantic frame classification task. For the Argument Identification task, we employ the RoBERTa PLM with T5 position encoding for extraction tasks. In the Role Identification task, we use the RoBERTa PLM with ALiBi position encoding for the classification task. Ultimately, our approach achieved a score of 71.41 in the closed track of the B leaderboard, securing fourth place and validating the effectiveness of our method.”</abstract>
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        <date>2024-07</date>
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%0 Conference Proceedings
%T Application of Entity Classification Model Based on Different Position Embedding in Chinese Frame Semantic Parsing
%A Huirong, Zhou
%A Sujie, Tian
%A Junbo, Li
%A Xiao, Yuan
%Y Lin, Hongfei
%Y Tan, Hongye
%Y Li, Bin
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F huirong-etal-2024-application
%X “This paper addresses three subtasks of Chinese Frame Semantic Parsing based on the BERT and RoBERTa pre-trained models: Frame Identification, Argument Identification, and Role Identification. In the Frame Identification task, we utilize the BERT PLM with Rotary Positional Encoding for the semantic frame classification task. For the Argument Identification task, we employ the RoBERTa PLM with T5 position encoding for extraction tasks. In the Role Identification task, we use the RoBERTa PLM with ALiBi position encoding for the classification task. Ultimately, our approach achieved a score of 71.41 in the closed track of the B leaderboard, securing fourth place and validating the effectiveness of our method.”
%U https://aclanthology.org/2024.ccl-3.2/
%P 10-20

Markdown (Informal)

[Application of Entity Classification Model Based on Different Position Embedding in Chinese Frame Semantic Parsing](https://aclanthology.org/2024.ccl-3.2/) (Huirong et al., CCL 2024)

• Application of Entity Classification Model Based on Different Position Embedding in Chinese Frame Semantic Parsing (Huirong et al., CCL 2024)

ACL

• Zhou Huirong, Tian Sujie, Li Junbo, and Yuan Xiao. 2024. Application of Entity Classification Model Based on Different Position Embedding in Chinese Frame Semantic Parsing. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 10–20, Taiyuan, China. Chinese Information Processing Society of China.