@inproceedings{li-etal-2021-ji,
title = "基于人物特征增强的拟人句要素抽取方法研究(Research on Element Extraction of Personified Sentences Based on Enhanced Characters)",
author = "Li, Jing and
Wang, Suge and
Chen, Xin and
Wang, Dian",
editor = "Li, Sheng and
Sun, Maosong and
Liu, Yang and
Wu, Hua and
Liu, Kang and
Che, Wanxiang and
He, Shizhu and
Rao, Gaoqi",
booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
month = aug,
year = "2021",
address = "Huhhot, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2021.ccl-1.55",
pages = "612--621",
abstract = "在散文阅读理解的鉴赏类问题中,对拟人句赏析考查比较频繁。目前,已有的工作仅对拟人句中的本体要素进行识别并抽取,存在要素抽取不完整的问题,尤其是当句子中出现多个本体时,需要确定拟人词与各个本体的对应关系。为解决这些问题,本文提出了基于人物特征增强的拟人句要素抽取方法。该方法利用特定领域的特征,增强句子的向量表示,再利用条件随机场模型对拟人句中的本体和拟人词要素进行识别。在此基础上,利用自注意力机制对要素之间的关系进行检测,使用要素同步机制和关系同步机制进行信息交互,用于要素识别和关系检测的输入更新。在自建的拟人数据集上进行{\textless}本体,拟人词{\textgreater}抽取的比较实验,结果表明本文提出的模型性能优于其他比较模型。",
language = "Chinese",
}
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<abstract>在散文阅读理解的鉴赏类问题中,对拟人句赏析考查比较频繁。目前,已有的工作仅对拟人句中的本体要素进行识别并抽取,存在要素抽取不完整的问题,尤其是当句子中出现多个本体时,需要确定拟人词与各个本体的对应关系。为解决这些问题,本文提出了基于人物特征增强的拟人句要素抽取方法。该方法利用特定领域的特征,增强句子的向量表示,再利用条件随机场模型对拟人句中的本体和拟人词要素进行识别。在此基础上,利用自注意力机制对要素之间的关系进行检测,使用要素同步机制和关系同步机制进行信息交互,用于要素识别和关系检测的输入更新。在自建的拟人数据集上进行\textless本体,拟人词\textgreater抽取的比较实验,结果表明本文提出的模型性能优于其他比较模型。</abstract>
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%0 Conference Proceedings
%T 基于人物特征增强的拟人句要素抽取方法研究(Research on Element Extraction of Personified Sentences Based on Enhanced Characters)
%A Li, Jing
%A Wang, Suge
%A Chen, Xin
%A Wang, Dian
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G Chinese
%F li-etal-2021-ji
%X 在散文阅读理解的鉴赏类问题中,对拟人句赏析考查比较频繁。目前,已有的工作仅对拟人句中的本体要素进行识别并抽取,存在要素抽取不完整的问题,尤其是当句子中出现多个本体时,需要确定拟人词与各个本体的对应关系。为解决这些问题,本文提出了基于人物特征增强的拟人句要素抽取方法。该方法利用特定领域的特征,增强句子的向量表示,再利用条件随机场模型对拟人句中的本体和拟人词要素进行识别。在此基础上,利用自注意力机制对要素之间的关系进行检测,使用要素同步机制和关系同步机制进行信息交互,用于要素识别和关系检测的输入更新。在自建的拟人数据集上进行\textless本体,拟人词\textgreater抽取的比较实验,结果表明本文提出的模型性能优于其他比较模型。
%U https://aclanthology.org/2021.ccl-1.55
%P 612-621
Markdown (Informal)
[基于人物特征增强的拟人句要素抽取方法研究(Research on Element Extraction of Personified Sentences Based on Enhanced Characters)](https://aclanthology.org/2021.ccl-1.55) (Li et al., CCL 2021)
ACL