A comparison of manual indexing and automatic indexing in the Humanities
By: by Dana Indra Sensuse.
Contributors: University of Toronto. Faculty of Information Studies.
Published: 2004
Language: eng
Number of pages: 236
Subjects: Humanities -- Abstracting and indexing.; Automatic indexing.
Description: There have been substantial studies comparing automatic indexing and manual indexing; however the results have been unclear as to whether automatic indexing systems can simulate what human indexers do. Some studies have claimed that an automatic indexing system is comparable to or even better than a manual indexing system, while others show that automatic indexing systems do not work as well as human indexers do. To address this contradiction further the present research focused on two primary questions. First, to what extent are sets of document content indicators generated by automatic indexing the same as those assigned by human indexers? Second, which of the two indexing methods captures the document content more accurately as assessed by independent judges?Results from the study suggest that automatic indexing terms are statistically significantly different (p &lt; 0.05) from manual indexing terms for the same abstracts. The term length in automatic indexing is shorter than that in manual indexing. The words chosen as terms by the automatic indexing system are also statistically significantly different (p &lt; 0.05) from those chosen by the manual indexing system. Manual indexing captured terms closer to those designated as "best terms" as compared with automatic indexing.Overall, findings from this study suggest that a combination of approaches results in an optimum representation of a document's contents. Furthermore, proper nouns should not be ignored in developing automatic systems for indexing in the area of the Humanities.There were 466 abstracts drawn from the Humanities and downloaded from University of Toronto Library databases. Of these, sixty abstracts were selected as samples. The samples were then randomly distributed to human indexers. Three professional indexers were employed as human indexers and the Copernic Summarizer(TM), which extracts single or multi words from a text, was used as the automatic indexing system. Each human indexer and the automatic indexing system assigned terms from the same documents. The terms generated from both indexing methods were combined and sorted alphabetically. Whichever two (of three) human indexers were not responsible for assigning index terms to a particular document, were asked to select the best terms from the list.
Notes: Advisers:  Clare Beghtol; Lynne Howarth.

Completed at the Faculty of Information Studies, University of Toronto.

Thesis (Ph.D.)--University of Toronto, 2004.

Includes bibliographical references (leaves 157-164)

Electronic version licensed for access by U. of T. users.

Source: Dissertation Abstracts International, Volume: 65-10, Section: A, page: 3611.