There is much work to be done in order to create a more effective word processing application that can truly scale and be truly collaborative as well.
Another functional area completely unaddressed by existing word processing applications is how to manage unstructured content, over and above search, to create role-based taxonomies (Adiego, Navarro, Fuente, 2007). Unstructured content represents the majority of data and information in organizations, often cited as being nearly 70% of all data (Wei, Yang, Lin, 2008). Word processing applications therefore need to have a latent semantic indexing (LSI) component that can successfully define and manage the interrelationships between data over time, building databases and data relationships that are relevant to the role taxonomies defined (Wei, Yang, Lin, 2008). This would in effect create a knowledge creation aspect to any word processing application, making it possible to immediately determine the relevancy of entire groups of documents, files and reports, and then providing pointers or reference points in individual taxonomies.
Joacquin Adiego, Gonzalo Navarro, & Pablo de la Fuente. (2007). Using structural contexts to compress semistructured text collections. Information Processing & Management, 43(3), 769.
Bai, B., Weston, J., Grangier, D., Collobert, R., Sadamasa, K., Qi, Y., Chapelle, O., & Weinberger, K.. (2010). Learning to rank with (a lot of) word features. Information Retrieval, 13(3), 291-314.
Hwang, J., & Ryu, K.. (2010). A weighted common structure-based clustering technique for XML documents. The Journal of Systems and Software, 83(7), 1267.
Marie Francine Moens, & Jos Dumortier. (2000). Text categorization: The assignment of subject descriptors to magazine articles. Information Processing & Management, 36(6), 841-861.
Rokaya, M., & Atlam, E.. (2010). Building of field association terms based on links. International Journal of Computer Applications…
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