In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
Algorithm Research & Explore
|
1623-1628

word2vec-ACV: word vector generation model of OOV context meaning

Wang Yonggui
Zheng Ze
Li Yue
College of Software, Liaoning Technical University, Huludao Liaoning 125105, China

Abstract

The word2vec model is a neural network model(NNLM) that converts words in text into a word vector. It is widely used in natural language processing tasks such as emotional analysis, question-answering robot and so on. Word vectors generated for the word2vec model lacked the ambiguity of context and the inability to create OOV word vectors. Based on the similarity information of document context and word2vec model, this paper proposed a word vector generation model called the word2vec-ACV model which conformed to the meaning of OOV context. The model was similar to the process of the word vector generated by the word2vec model. First of all, base on the continuous word bag(CBOW) and the Hierarchical softmax, the word2vec model trained the word vector matrix, namely the weight matrix. Secondly, normalized the co-occurrence matrix to get the average context word vector. Then, the word vector consisted of an average context word vector matrix. Finally, it multiplied the average context word vector matrix and the weight matrix to get the word vector matrix. In order to simultaneously solve the ambiguity problem of out of vocabulary words and out of vocabulary words to create, this paper divided the average context word vectors into the global average context word vector(global ACV) and the local average context word vector(local ACV). In addition, the two taken the weight value to form a new average context word vector matrix. The word2vec model could effectively express the word in vector form. Experiments on analogical tasks and named entity recognition(NER) tasks respectively, the results show that the word2vec-ACV model is superior to the word2vec model in the accurate expression of the word vector. It is a word vector representation method to create a contextual context for OOV words.

Foundation Support

国家自然科学基金青年基金资助项目(61404069)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.12.0800
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 6
Section: Algorithm Research & Explore
Pages: 1623-1628
Serial Number: 1001-3695(2019)06-005-1623-06

Publish History

[2019-06-05] Printed Article

Cite This Article

王永贵, 郑泽, 李玥. word2vec-ACV:OOV语境含义的词向量生成模型 [J]. 计算机应用研究, 2019, 36 (6): 1623-1628. (Wang Yonggui, Zheng Ze, Li Yue. word2vec-ACV: word vector generation model of OOV context meaning [J]. Application Research of Computers, 2019, 36 (6): 1623-1628. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)