Resumen

FITZGERALD, Kyle Andrew; LA HARPE, Andre Charles de; UYS, Corrie Susanna  y  BYTHEWAY, Andrew John. Information retrieval: Solving mismatching vocabulary in closed document collections. SAJLIS [online]. 2021, vol.87, n.2, pp.42-54. ISSN 2304-8263.  http://dx.doi.org/10.7553/10.7553/87-2-1957.

During a search, phrase-terms expressed in queries are presented to an information retrieval system (IRS) to find documents relevant to a topic. The IRS makes relevance judgements by attempting to match vocabulary in queries to documents. If there is a mismatch, the problem of vocabulary mismatch occurs. The aim is to examine ways of searching for documents more effectively, in order to minimise mismatches. A further aim is to understand the mechanisms of, and the differences between, human and machine-assisted, retrieval. The objective of this study was to determine whether IRS-H (an IRS using the hybrid indexing method) and human participants agree or disagree on relevancy judgments, and whether the problem of mismatching vocabulary can be solved. A collection of eighty research documents and sixty-five phrase-terms were presented to (i) IRS-H and four participants in Test 1, and (ii) IRS-H and one participant (aided by search software) in Test 2. Statistical analysis was performed using the Kappa coefficient. IRS-H and the four participants' judgements disagreed. IRS-H and the participant aided by search software judgments did agree. IRS-H solves the problem of mismatching vocabulary between a query and a document.

Palabras clave : Information retrieval; hybrid indexing method; precision; recall; research; vocabulary mismatch.

        · texto en Inglés     · Inglés ( pdf )