Automatic indexing

Automatic indexing is the ability for a computer to scan large volumes of documents against a controlled vocabulary, taxonomy, thesaurus or ontology and use those controlled terms to quickly and effectively index large document depositories. As the number of documents exponentially increases with the proliferation of the Internet, automatic indexing will become essential to maintaining the ability to find relevant information in a sea of irrelevant information. Automatic Indexing is the process of analyzing an item to extract the information to be permanently kept in an index.

The automated process can encounter problems and these are primarily caused by two factors: 1) the complexity of the language; and, 2) the lack intuitiveness and the difficulty in extrapolating concepts out of statements on the part of the computing technology.[1] These are primarily linguistic challenges and specific problems involve semantic and syntactic aspects of language.[1]

History

There are scholars who cite that the subject of automatic indexing attracted attention as early as the 1950s, particularly with the demand for faster and more comprehensive access to scientific and engineering literature.[2] This was highlighted by the information explosion, which was predicted in the 1960s[3] and came about through the emergence of information technology and the World Wide Web. This phenomenon required the development of an indexing system that can cope with the challenge of storing and organizing vast amount of data and can facilitate information access.[4][5] New electronic hardware further advanced automated indexing since it overcame the barrier imposed by old paper archives, allowing the encoding of information at the molecular level.[3] The automatic indexing is also partly driven by the emergence of the field called computational linguistics, which steered research that eventually produced techniques such as the application of computer analysis to the structure and meaning of languages.[2][6] Automatic indexing is further spurred by research and development in the area of artificial intelligence and self-organizing system also referred to as thinking machine.[2]



See also

References

  1. 1 2 Cleveland, Ana; Cleveland, Donald (2013). Introduction to Indexing and Abstracting: Fourth Edition. Santa Barbara, CA: ABC-CLIO. p. 289. ISBN 9781598849769.
  2. 1 2 3 Riaz, Muhammad (1989). Advanced Indexing and Abstracting Practies. Delhi: Atlantic Publishers & Distributors. p. 263.
  3. 1 2 Torres-Moreno, Juan-Manuel (2014). Automatic Text Summarization. Hoboken, NJ: John Wiley & Sons. pp. xii. ISBN 9781848216686.
  4. ↑ Kapetanios, Epaminondas; Sugumaran, Vijayan; Natural Language and Information Systems: 13th International Conference on Applications of Natural Language to Information Systems, NLDB 2008 London, UK, June 24-27, 2008, Proceedings, Myra (2008). Natural Language and Information Systems: 13th International Conference on Applications of Natural Language to Information Systems, NLDB 2008 London, UK, June 24-27, 2008, Proceedings. Berlin: Springer Science & Business Media. p. 350. ISBN 3540698574.
  5. ↑ Basch, Reva (1996). Secrets of the Super Net Searchers: The Reflections, Revelations, and Hard-won Wisdom of 35 of the World's Top Internet Researchers. Medford, NJ: Information Today, Inc. p. 271. ISBN 0910965226.
  6. ↑ Armstrong, Susan (1994). Using Large Corpora. Cambridge, MA: MIT Press. p. 291. ISBN 0262510820.
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