1. By Steve Shellman

2.  The president, CEO, and chief scientist at
Strategic Analysis Enterprises, Inc., Steve
Shellman is a leading expert in event
forecasting, sentiment analysis, and
qualitative and quantitative analysis. Using
methodologies such as natural language
processing and entity extraction, Stephen M.
Shellman generates and analyzes valuable
organizational data pertaining to events and
group behavior.

3.  The information extraction strategy known as entity
extraction or named-entity recognition (NER) concerns
itself with detecting and classifying certain elements in a
string of text contained within a natural language
document. These elements may be identified as places,
quantities, time expressions, or names of persons or
locations.
In addition to identifying entities such as locations, names,
and places, named-entity extraction can detect temporal
and numerical expressions. Temporal expressions denote
frequency, time, or duration, whereas numerical
expressions could be percentages or currencies. Analysts
employ a number of algorithms to infer classification from
chunks of text, including beam search algorithms, left to
right decoding, or Viterbi.

4.  Some of the most common entity extraction
techniques used in NER include simple
pattern-based extraction, dictionary-based
extraction, hybrid pattern extraction,
hierarchal context, and formatting cues.