Validating Syntactic Correctness Using Unsupervised Clustering Algorithms

被引:0
|
作者
Noh, Sanguk [1 ]
Chung, Kihyun [2 ]
Shim, Jaebock [3 ]
机构
[1] Catholic Univ Korea, Sch Comp Sci & Informat Engn, Bucheon Si 14662, South Korea
[2] Ajou Univ, Div Elect Engn, Suwon 16499, South Korea
[3] Deltaindex Inc, Daejeon 34027, South Korea
关键词
recommendation of syntactically correct sentence; unsupervised clustering algorithms; autoencoding procedure; software requirement specifications; CLASSIFICATION;
D O I
10.3390/electronics11142113
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
When developing a complex system in an open platform setting, users need to compose and maintain a systematic requirement specification. This paper proposes a solution to guarantee a syntactically accurate requirement specification that minimizes the ambiguity caused by ungrammatical sentences. Our system has a set of standard jargon and templates that are used as a guideline to write grammatically correct sentences. Given a database of standard technical Korean (STK) templates, the system that we have designed and implemented divides a new sentence into a specific cluster. If the system finds an identical template in a cluster, it confirms the new sentence as a sound one. Otherwise, the system uses unsupervised clustering algorithms to return the template that most closely resembles the syntax of the inputted sentence. We tested our proposed system in the field of open platform development for a railway train. In the experiment, our system learned to partition templates into clusters while reducing null attributes of an instance using the autoencoding procedure. Given a set of clusters, the system was able to successfully recommend templates that were syntactically similar to the structure of the inputted sentence. Since the degree of similarity for 500 instances was 97.00% on average, we conclude that our robust system can provide an appropriate template that users can use to modify their syntactically incorrect sentences.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] Hybrid Multiobjective Evolutionary Algorithms for Unsupervised QPSO, BBPSO and Fuzzy clustering
    Lai, Daphne Teck Ching
    Sato, Yuji
    2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), 2021, : 696 - 703
  • [42] Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
    Ghosh, Ashish
    Mishra, Niladri Shekhar
    Ghosh, Susmita
    INFORMATION SCIENCES, 2011, 181 (04) : 699 - 715
  • [43] Unsupervised texture segmentation based on immune genetic algorithms and fuzzy clustering
    Li, Ma
    Staunton, R. C.
    2006 8TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-4, 2006, : 957 - +
  • [44] Assessment of clustering algorithms for unsupervised transcription factor binding site discovery
    Karabulut, Mustafa
    Ibrikci, Turgay
    EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (09) : 11160 - 11166
  • [45] A Review of Unsupervised K-Value Selection Techniques in Clustering Algorithms
    Pegado-Bardayo, Ana
    Lorenzo-Espejo, Antonio
    Munuzuri, Jesus
    Escudero-Santana, Alejandro
    JOURNAL OF INDUSTRIAL ENGINEERING AND MANAGEMENT-JIEM, 2024, 17 (03): : 641 - 649
  • [46] Validating an unsupervised weightless perceptron
    Wickert, I
    França, FMG
    ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING: COMPUTATIONAL INTELLIGENCE FOR THE E-AGE, 2002, : 537 - 541
  • [47] Grammar Fragment acquisition using syntactic and semantic clustering
    Arai, K
    Wright, JH
    Riccardi, G
    Gorin, AL
    SPEECH COMMUNICATION, 1999, 27 (01) : 43 - 62
  • [48] Optimising Video Summaries Using Unsupervised Clustering
    Ren, Kan
    Fernando, W. A. C.
    Calic, Janko
    PROCEEDINGS ELMAR-2008, VOLS 1 AND 2, 2008, : 451 - 454
  • [49] SUBSPACE CLUSTERING USING UNSUPERVISED DATA AUGMENTATION
    Abdolali, Maryam
    Gillis, Nicolas
    2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 3868 - 3872
  • [50] Interpretable clustering using unsupervised binary trees
    Fraiman, Ricardo
    Ghattas, Badih
    Svarc, Marcela
    ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 2013, 7 (02) : 125 - 145