A Field Evaluation of Natural Language for Data Retrieval

Matthias Jarke, Jon A. Turner, Edward A. Stohr, Yannis Vassiliou, Norman H. White, Ken Michielsen

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Although a large number of natural language database interfaces have been developed, there have been few empirical studies of their practical usefulness. This paper presents the design and results of a field evaluation of a natural language system-NLS-used for data retrieval. A balanced multifactorial design comparing NLS to a reference retrieval language SQL is described. The data are analyzed on two levels: Work task (n=87) and query (n=1081). SQL performed better than NLS on a variety of measures, but NLS required less effort to use. Subjects performed much poorer than expected based on the results of laboratory studies. This finding is attributed to the complexity of the field setting and to optimism in grading laboratory experiments. The methodology developed for studying computer languages in real work settings was successful in consistently measuring differences in treatments over a variety of conditions.

Original languageEnglish
Pages (from-to)97-114
Number of pages18
JournalIEEE Transactions on Software Engineering
VolumeSE-11
Issue number1
DOIs
StatePublished - Jan 1985

Keywords

  • Human-machine interaction
  • interface design
  • language evaluation
  • natural language query
  • query languages

Fingerprint

Dive into the research topics of 'A Field Evaluation of Natural Language for Data Retrieval'. Together they form a unique fingerprint.

Cite this