Semantic Similarity of Inverse Morpheme Words Based on Word Embedding

被引:0
|
作者
Zhou, Jiaomei [1 ]
Liu, Zhiying [1 ]
机构
[1] Beijing Normal Univ, Inst Chinese Informat Proc, Beijing, Peoples R China
来源
CHINESE LEXICAL SEMANTICS, CLSW 2021, PT I | 2022年 / 13249卷
关键词
Inverse morpheme words; Semantic similarity; Word embedding;
D O I
10.1007/978-3-031-06703-7_35
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Inverse morpheme words are compound words that have the same morphemes but are arranged in the opposite order. The majority of related works on the subject have focused on a narrow investigation of dictionary definitions, with few studies based on large-scale corpora. Based on the People's Daily corpus (1946-2017), we add and delete words from a base list and then obtained a word list consisting of 668 pairs of inverse morpheme words. Furthermore, we also calculated cosine similarity by using word embedding based on the distributed representation and discovered that 76% of inverse morpheme words have a cosine similarity of 0.4 or higher, and that word formation, part-of-speech, and frequency all have an impact on semantic similarity.
引用
收藏
页码:452 / 463
页数:12
相关论文
共 50 条
  • [21] Exploration of a Threshold for Similarity Based on Uncertainty in Word Embedding
    Rekabsaz, Navid
    Lupu, Mihai
    Hanbury, Allan
    ADVANCES IN INFORMATION RETRIEVAL, ECIR 2017, 2017, 10193 : 396 - 409
  • [22] Word Embedding-Based Topic Similarity Measures
    Terragni, Silvia
    Fersini, Elisabetta
    Messina, Enza
    NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS (NLDB 2021), 2021, 12801 : 33 - 45
  • [23] Image similarity measures based on weak semantic embedding
    Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China
    Gaojishu Tongxin, 2006, 1 (27-31):
  • [24] Document similarity calculation based on words' semantic information
    School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan , China
    Li, Sheng, 1600, Binary Information Press (10):
  • [25] Word Clustering based on Word2vec and Semantic Similarity
    Luo Jie
    Wang Qinglin
    Li Yuan
    2014 33RD CHINESE CONTROL CONFERENCE (CCC), 2014, : 517 - 521
  • [26] Word Semantic Similarity Calculation Based on Word2vec
    Jin, Xiaolin
    Zhang, Shuwu
    Liu, Jie
    2018 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND INFORMATION SCIENCES (ICCAIS), 2018, : 12 - 16
  • [27] An Improved Algorithm of Word Semantic Similarity Based on HowNet
    Kang, Bocheng
    Qi, Junpeng
    2022 16TH IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP2022), VOL 1, 2022, : 266 - 271
  • [28] Word Semantic Similarity Research Based on Latent Relationships
    Lin, Xiaoqing
    Wang, Danling
    2013 2ND INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION AND MEASUREMENT, SENSOR NETWORK AND AUTOMATION (IMSNA), 2013, : 168 - 171
  • [29] Measuring Word Semantic Similarity Based on Transferred Vectors
    Li, Changliang
    Ma, Teng
    Zhou, Yujun
    Cheng, Jian
    Xu, Bo
    NEURAL INFORMATION PROCESSING (ICONIP 2017), PT IV, 2017, 10637 : 326 - 335
  • [30] Sentence Semantic Similarity based on Word FiImbedding and WordNet
    Farouk, Mamdouh
    PROCEEDINGS OF 2018 13TH INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND SYSTEMS (ICCES), 2018, : 33 - 37