Key word indexing models, Humanities

KEY WORD INDEXING MODELS 

Computers began to be used to aid information retrieval system in the 1950s. The first use of computers in information  retrieval was  the production of indexes. The Central  Intelligence Agency (CIA) of the USA is said to be the  first organisation to use the machine produced Key Words from Title Index since 1952. H P Luhn and his associates produced permuted title indexes in the International Conference of Scientific Information held at Washington in 1958. Luhn  named his index as '"Key-Word-in-Context" (KWIC) index and reported its method of generation in a paper in 1959. The success of KWIC was  established after its adoption by American Chemical Society in 1961 in its publication "Chemical Titles". "Keyword" means subject denoting words, chosen mainly from the titles and/or sometimes from abstract or text of the document for the purpose of indexing. The words chosen may be single words, multi-words or even phrases that convey content information of the document. However, the system developed by Luhn was  from the words in the title of a document. Several keywords  may be chosen for a  document to provide access form different approaches  of the user. Since the  keyword indexing is based  on natural language terminology  of the documents, this system is also known as "Natural Language Indexing System". 

The KWIC index, developed by H P Luhn, is said to be one of the earliest and successful computer-generated keyword indexes. In his method, he suggested the selection of words from the title excepting the unwanted or insignificant words. While the words will form index term, other words in the title, what he said as will be wrapped around it". These words will serve as the context. KWIC indexing system  is based on usage of natural language terminology  to generate the index entries.  All of the words in the titles of a batch of documents for which an index is   required are matched, by a computer against a stop-list. This stop-list or stop-wordlist is a record of words which are insignificant in an index. They include words like articles, auxiliary verbs together with such general words  as "aspect",  "different", "method", "very", etc. Depending upon the subject orientation of each  major search system has defined their own list of "stop-words". Some words which might be feasible access points in a general index prove worthless  in an index  devoted to a special  subject area. Indiscriminate  marking articles, prepositions, etc.  may create  problems because of important scientific and technical terms such as "Vitamin A", "On line", etc. In view of this, words to be included in the list of "stop-words" are required to be selected in the light of the subject orientation of the index. Stop-words do not appear as entry words but they are displayed in the titles in the index in order to provide the context of the document. No controlled vocabulary is required for keyword indexing. Indexing terms are  selected from the natural language of documents. In addition to KWIC index, there are a number of varieties of keyword indexes that have been developed over  the years. Two most important versions are Key Word Out of Context (KWOC) and Key Word And Context (KWAC). They differ only in terms  of their formats but indexing principles/techniques remain more or less same. 

Posted Date: 10/26/2012 3:42:53 AM | Location : United States







Related Discussions:- Key word indexing models, Assignment Help, Ask Question on Key word indexing models, Get Answer, Expert's Help, Key word indexing models Discussions

Write discussion on Key word indexing models
Your posts are moderated
Related Questions
What is the method used to solve an Ethical problem?  Recognizing a problem or its requirements. Gathering information and explaining the problem to be solved or goal to be ach

Unit Card System   In an added entry we may record all the information that we have recorded in the Main Entry. Or, we may prefer to be brief in 4.2 Unit Card our description s


Identification of Parts   In the light of what we have described so far let us identify the various areas of description and the data elements in the following entry. The areas

Objectives   The objectives of centralised cataloguing are to: avoid duplication of work;  achieve uniform and standard cataloguing practices;  minimize the cost of

Is decentralization a panacea to African development challenges

discusss the steps in cataloguing process

Besides the germans, who were the largest of the non england immigrant grouos in the north

Functions of a Subject Authority File   The functions of a subject authority file are discussed below:  Cataloguing: The subject authority file serves as the source of ind

Comprehensive Services Centralised agencies in countries such as the former USSR, France, Japan and China have been attempting comprehensive I & A services to cover all types