Building natural language responses from natural language questions in the spatio-temporal context

被引:0
|
作者
Landoulsi G. [1 ]
Mahmoudi K. [1 ]
Faïz S. [1 ]
机构
[1] Laboratory of Remote Sensing and Information Systems with Spatial References (LTSIRS), Enit National Engineering School of Tunis, Tunis El Manar University
关键词
GDBs; Geographic databases; Natural language generation; NLG; Question answering systems; Spatio-temporal data; Structured query language;
D O I
10.1504/IJIIDS.2021.112077
中图分类号
学科分类号
摘要
With the evolving research in geographic information system (GIS) owing to its ability to support decision makers in different fields, there is a strong need to enabling all users; specialists and non-specialists to profit from this technology. Although, the key impediment to non-specialists is the language to interact with the GIS and especially its embedded geographic database (GDB) which require SQL skills. In this paper we explore a new approach which alleviates nomad GIS users from any formatting effort by only using the natural language as a GDB communication mean. The process is generally two-fold: 1) formatting the natural language user query to be processed by the GDB engine; 2) translating the GDB retrieved answer to a text easily interpreted by all GIS users. The resulting implemented system was integrated to the OpenJump GIS and has been evaluated to give satisfactory results. © 2021 Inderscience Enterprises Ltd.
引用
收藏
页码:1 / 25
页数:24
相关论文
共 50 条
  • [31] Evaluation in the context of natural language generation
    Mellish, C
    Dale, R
    COMPUTER SPEECH AND LANGUAGE, 1998, 12 (04): : 349 - 373
  • [32] Building natural language generation systems
    Horacek, H
    COMPUTATIONAL LINGUISTICS, 2001, 27 (02) : 298 - 300
  • [33] Prior and temporal sequences for natural language
    Tim Fernando
    Synthese, 2016, 193 : 3625 - 3637
  • [34] Prior and temporal sequences for natural language
    Fernando, Tim
    SYNTHESE, 2016, 193 (11) : 3625 - 3637
  • [35] Temporal Reasoning in Natural Language Inference
    Vashishtha, Siddharth
    Poliak, Adam
    Lal, Yash Kumar
    Van Durme, Benjamin
    White, Aaron Steven
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EMNLP 2020, 2020, : 4070 - 4078
  • [36] QUESTIONS NATURAL-LANGUAGE EXAMPLES IN CADUCEUS
    BOYCE, BR
    ONLINE, 1986, 10 (02): : 54 - 54
  • [37] Getting answers to natural language questions on the Web
    Radev, DR
    Libner, K
    Fan, WG
    JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 2002, 53 (05): : 359 - 364
  • [38] Predicting Difficulty and Discrimination of Natural Language Questions
    Byrd, Matthew A.
    Srivastava, Shashank
    PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022): (SHORT PAPERS), VOL 2, 2022, : 119 - 130
  • [39] Spatio-temporal responses of black-tailed gulls to natural and anthropogenic food resources
    Yoda, Ken
    Tomita, Naoki
    Mizutani, Yuichi
    Narita, Akira
    Niizuma, Yasuaki
    MARINE ECOLOGY PROGRESS SERIES, 2012, 466 : 249 - 259
  • [40] Mapping natural language questions to medical specialties
    Bortanoiu, Nicoleta Denisa
    Radoi, Ion Emilian
    2020 19TH ROEDUNET CONFERENCE: NETWORKING IN EDUCATION AND RESEARCH (ROEDUNET), 2020,