Character-Level Convolutional Neural Network for Paraphrase Detection and Other Experiments

被引:2
|
作者
Maraev, Vladislav [1 ]
Saedi, Chakaveh [1 ]
Rodrigues, Joao [1 ]
Branco, Antonio [1 ]
Silva, Joao [1 ]
机构
[1] Univ Lisbon, Fac Sci, Dept Informat, Lisbon, Portugal
关键词
Paraphrase detection; Word embeddings; Character embeddings; Convolutional neural networks; Distributional semantics;
D O I
10.1007/978-3-319-71746-3_23
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The central goal of this paper is to report on the results of an experimental study on the application of character-level embeddings and basic convolutional neural network to the shared task of sentence paraphrase detection in Russian. This approach was tested in the standard run of Task 2 of that shared task and revealed competitive results, namely 73.9% accuracy against the test set. It is compared against a word-level convolutional neural network for the same task, and varied other approaches, such as rule-based and classical machine learning.
引用
收藏
页码:293 / 304
页数:12
相关论文
共 50 条
  • [21] Weakly-supervised character-level convolutional neural networks for text classification
    Liu, Yongsheng
    Chen, Wenyu
    Niyongabo, Rubungo Andre
    Qu, Hong
    Miao, Kebin
    Wei, Feng
    DEVELOPMENTS OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN COMPUTATION AND ROBOTICS, 2020, 12 : 701 - 708
  • [22] Character-Level Attention Convolutional Neural Networks for Short-Text Classification
    Yin, Feiyang
    Yao, Zhilin
    Liu, Jia
    HUMAN CENTERED COMPUTING, 2019, 11956 : 560 - 567
  • [23] Character-level convolutional networks for arithmetic operator character recognition
    Liang, Zhijie
    Li, Qing
    Liao, Shengbin
    FIFTH INTERNATIONAL CONFERENCE ON EDUCATIONAL INNOVATION THROUGH TECHNOLOGY (EITT 2016), 2016, : 208 - 212
  • [24] Automatically Classifying Chinese Judgment Documents Using Character-Level Convolutional Neural Networks
    Zhou, Xiaosong
    Li, Chuanyi
    Ge, Jidong
    Li, Zhongjin
    Zhou, Xiaoyu
    Luo, Bin
    PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT II, 2018, 11013 : 430 - 437
  • [25] MOJI: Character-level convolutional neural networks for Malicious Obfuscated Java']JavaScript Inspection
    Ishida, Minato
    Kaneko, Naoshi
    Sumi, Kazuhiko
    APPLIED SOFT COMPUTING, 2023, 137
  • [26] Text Classification and Transfer Learning Based on Character-Level Deep Convolutional Neural Networks
    Sato, Minato
    Orihara, Ryohei
    Sei, Yuichi
    Tahara, Yasuyuki
    Ohsuga, Akihiko
    AGENTS AND ARTIFICIAL INTELLIGENCE (ICAART 2017), 2018, 10839 : 62 - 81
  • [27] Neural Character-Level Syntactic Parsing for Chinese
    Li, Zuchao
    Zhou, Junru
    Zhao, Hai
    Zhang, Zhisong
    Li, Haonan
    Ju, Yuqi
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2022, 73 : 461 - 509
  • [28] Neural Character-Level Syntactic Parsing for Chinese
    Li Z.
    Zhou J.
    Zhao H.
    Zhang Z.
    Li H.
    Ju Y.
    Journal of Artificial Intelligence Research, 2022, 73 : 461 - 509
  • [29] Neural Character-Level Dependency Parsing for Chinese
    Li, Haonan
    Zhang, Zhisong
    Ju, Yuqi
    Zhao, Hai
    THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 5205 - 5212
  • [30] Character-Aware Convolutional Neural Networks for Paraphrase Identification
    Huang, Jiangping
    Ji, Donghong
    Yao, Shuxin
    Huang, Wenzhi
    NEURAL INFORMATION PROCESSING, ICONIP 2016, PT II, 2016, 9948 : 177 - 184