Using SGML as a Basis for Data-Intensive Natural Language Processing

被引:0
|
作者
D. McKelvie
C. Brew
H.S. Thompson
机构
[1] University of Edinburgh,Language Technology Group, Human Communication Research Centre
来源
关键词
corpus-based linguistics; natural language processing; SGML;
D O I
暂无
中图分类号
学科分类号
摘要
This paper describes the LT NSL system (McKelvie et al., 1996), an architecture for writing corpus processing tools. This system is then compared with two other systems which address similar issues, the GATE system (Cunningham et al., 1995) and the IMS Corpus Workbench (Christ, 1994). In particular we address the advantages and disadvantages of an SGML approach compared with a non-sgml database approach.
引用
收藏
页码:367 / 388
页数:21
相关论文
共 50 条
  • [41] Natural Language Processing and Big Data
    Monti, Johanna
    Monteleone, Mario
    di Buono, Maria Pia
    Marano, Federica
    2013 ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM), 2013, : 725 - 731
  • [42] Using Semantics to Personalize Access to Data-Intensive Web Sources
    Starzecka, Monika
    Walczak, Adam
    BUSINESS INFORMATION SYSTEMS WORKSHOPS, 2009, 37 : 28 - 38
  • [43] Processing normative references on the basis of natural language questions
    Palmirani, M
    Brighi, R
    Massini, M
    15TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, 2004, : 9 - 12
  • [44] Crawling Data-Intensive Web Sources Using Structure Information
    Weckowski, Dawid Grzegorz
    BUSINESS INFORMATION SYSTEMS WORKSHOPS, BIS 2013, 2013, 160 : 196 - 207
  • [45] Rethinking Data-Intensive Science Using Scalable Analytics Systems
    Nothaft, Frank Austin
    Massie, Matt
    Danford, Timothy
    Zhang, Zhao
    Laserson, Uri
    Yeksigian, Carl
    Kottalam, Jey
    Ahuja, Arun
    Hammerbacher, Jeff
    Linderman, Michael
    Franklin, Michael J.
    Joseph, Anthony D.
    Patterson, David A.
    SIGMOD'15: PROCEEDINGS OF THE 2015 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2015, : 631 - 646
  • [46] Understanding and Minimizing Disk Contention Effects for Data-Intensive Processing in Virtualized Systems
    Matteussi, Kassiano J.
    Resin Geyer, Claudio Fernando
    Xavier, Miguel G.
    De Rose, Cesar A. F.
    PROCEEDINGS 2018 INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING & SIMULATION (HPCS), 2018, : 901 - 908
  • [47] Building architectures for data-intensive science using the ADAGE framework
    Yao, Lawrence
    Rabhi, Fethi A.
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2015, 27 (05): : 1188 - 1206
  • [48] Data replication techniques for data-intensive applications
    No, Jaechun
    Park, Chang Won
    Park, Sung Soon
    COMPUTATIONAL SCIENCE - ICCS 2006, PT 4, PROCEEDINGS, 2006, 3994 : 1063 - 1070
  • [49] Analysis of Big Data for Data-Intensive Applications
    Dave, Meenu
    Gianey, Hemant Kumar
    2016 INTERNATIONAL CONFERENCE ON RECENT ADVANCES AND INNOVATIONS IN ENGINEERING (ICRAIE), 2016,
  • [50] Data-intensive computing and digital libraries
    Moore, R
    Prince, TA
    Ellisman, M
    COMMUNICATIONS OF THE ACM, 1998, 41 (11) : 56 - 62