Information extraction

here may be more text data in electronic form than ever before, but much of it is ignored. No human can read, understand, and synthesize megabytes of text on an everyday basis. Missed information— and lost opportunities—has spurred researchers to explore various information management strategies to establish order in the text wilderness. The most common strategies are information retrieval (IR) and information filtering [4]. A relatively new development—information extraction (IE)—is the subject of this article. We can view IR systems as combine harvesters that bring back useful material from vast fields of raw material. With large amounts of potentially useful information in hand, an IE system can then transform the raw material, refining and reducing it to a germ of the original text (see Figure 1). Suppose financial analysts are investigating production of semiconductor devices (see Figure 2). They might want to know several things: