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Essay / Research Paper Abstract
A 10 page research paper that discusses in detail a natural language processing application, a program called "AutoSlog," which automatically creates dictionaries once constructed manually, with a high degree of accuracy. Bibliography lists 2 sources.
Page Count:
10 pages (~225 words per page)
File: D0_khnlp.rtf
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Unformatted sample text from the term paper:
new data and reducing it into figures and facts that can be easily utilized in policy and decision making. But what if there was a method for automating this process?
This is where natural language processing systems comes into play and offers reliable, practical solutions towards such automation. A principal goal of natural language processing research has been to
develop a "knowledge-based natural language processing (NLP) system," which is applicable across domains (Riloff, 1996, p. 101). However, the majority of knowledge-based NLP systems are predicated on a domain-specific dictionary
of ideas, which results in a considerable "knowledge-engineering bottleneck" (Riloff, 1996, p. 101). In response to this, Riloff (1996) and her research team at the University of Utah developed a
system that they refer to as "AutoSlog," which addresses this bottleneck through a process called "information extraction" (p. 101). Information extraction systems are generally dependent on a dictionary of
extraction patterns in order to identify relevant data (University of Utah). In the vast majority of systems, this sort of dictionary is formulated manually, which is of course extremely tiresome
and time-consuming activity. In contrast to this system, AutoSlog employs an annotated corpus and simple linguistic rules. AutoSlog has been utilized to create dictionaries for three different domains: terrorism,
joint ventures and microelectronics (University of Utah). In regards to terrorism, AutoSlog produced a dictionary after only five person-hours of effort that equated with 98 percent accuracy to the performance
of a hand-crafted dictionary that utilized roughly 1500 person-hours for its completion (University of Utah). In reporting on this research, Riloff (1996) offers a detailed description of this NLP application,
comparing its performance across three domains, as well as the lessons learned abut this approach and how even novice users can generate reliable dictionaries with AutoSlog. Knowledge-based NLP systems Knowledge-based
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