Towards Syntax-Aware Editors for Visual Languages

被引:0
|
作者
Costagliola, Gennaro [1 ]
Deufemia, Vincenzo [1 ]
Polese, Giuseppe [1 ]
机构
[1] Univ Salerno, Dipartimento Matemat & Informat, Fisciano, SA, Italy
关键词
Visual language parsing; error-handling; syntax-aware editing;
D O I
10.1016/j.entcs.2004.08.050
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Editors for visual languages should provide a user-friendly environment supporting end users in the composition of visual sentences in an effective way. Syntax-aware editors are a class of editors that prompt users into writing syntactically correct programs by exploiting information on the visual language syntax. In particular, they do not constrain users to enter only correct syntactic states in a visual sentence. They merely inform the user when visual objects are syntactically correct. This means detecting both syntax and potential semantic errors as early as possible and providing feedback on such errors in a non-intrusive way during editing. As a consequence, error handling strategies are an essential part of such editing style of visual sentences. In this work, we develop a strategy for the construction of syntax-aware visual language editors by integrating incremental subsentence parsers into free-hand editors. The parser combines the LR-based techniques for parsing visual languages with the more general incremental Generalized LR parsing techniques developed for string languages. Such approach has been profitably exploited for introducing a noncorrecting error recovery strategy, and for prompting during the editing the continuation of what the user is drawing.
引用
收藏
页码:107 / 125
页数:19
相关论文
共 50 条
  • [1] Building syntax-aware editors for visual languages
    Costagliola, G
    Deufemia, V
    Polese, G
    Risi, M
    [J]. JOURNAL OF VISUAL LANGUAGES AND COMPUTING, 2005, 16 (06): : 508 - 540
  • [2] Towards syntax-aware token embeddings
    Popa, Diana Nicoleta
    Perez, Julien
    Henderson, James
    Gaussier, Eric
    [J]. NATURAL LANGUAGE ENGINEERING, 2021, 27 (06) : 691 - 720
  • [3] Syntax-aware Neural Semantic Role Labeling for Morphologically Rich Languages
    Vasic, Daniel
    Vasic, Mirela Kundid
    [J]. 2020 28TH INTERNATIONAL CONFERENCE ON SOFTWARE, TELECOMMUNICATIONS AND COMPUTER NETWORKS (SOFTCOM), 2020, : 327 - 332
  • [4] A Syntax-Aware Encoder for Authorship Attribution
    Liu, Jianbo
    Hu, Zhiqiang
    Zhang, Jiasheng
    Lee, Roy Ka-Wei
    Shao, Jie
    [J]. WEB INFORMATION SYSTEMS ENGINEERING - WISE 2021, PT I, 2021, 13080 : 403 - 411
  • [5] Syntax-Aware Representation for Aspect Term Extraction
    Zhang, Jingyuan
    Xu, Guangluan
    Wang, Xinyi
    Sun, Xian
    Huang, Tinglei
    [J]. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2019, PT I, 2019, 11439 : 123 - 134
  • [6] Syntax-Aware Mutation for Testing the Solidity Compiler
    Mitropoulos, Charalambos
    Sotiropoulos, Thodoris
    Ioannidis, Sotiris
    Mitropoulos, Dimitris
    [J]. COMPUTER SECURITY - ESORICS 2023, PT III, 2024, 14346 : 327 - 347
  • [7] Syntax-aware Multilingual Semantic Role Labeling
    He, Shexia
    Li, Zuchao
    Zhao, Hai
    [J]. 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019): PROCEEDINGS OF THE CONFERENCE, 2019, : 5350 - 5359
  • [8] Syntax-Aware Neural Semantic Role Labeling
    Xia, Qingrong
    Li, Zhenghua
    Zhang, Min
    Zhang, Meishan
    Fu, Guohong
    Wang, Rui
    Si, Luo
    [J]. THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, : 7305 - 7313
  • [9] Syntax-aware on-the-fly code completion
    Takerngsaksiri, Wannita
    Tantithamthavorn, Chakkrit
    Li, Yuan-Fang
    [J]. INFORMATION AND SOFTWARE TECHNOLOGY, 2024, 165
  • [10] Improving BERT with Syntax-aware Local Attention
    Li, Zhongli
    Zhou, Qingyu
    Li, Chao
    Xu, Ke
    Cao, Yunbo
    [J]. FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, ACL-IJCNLP 2021, 2021, : 645 - 653