/**
 * Contains annotators that are based on statistical methods.
 *
 * <p>Statistical methods for entity extraction generally involve training a model to recognise what
 * a certain entity type, for example 'People', look like within the context of a sentence or a
 * document.
 *
 * <p>This allows for a very flexible approach to entity extraction, where we don't require prior
 * knowledge of what entities are likely to appear, but does require that models are trained on
 * appropriate training data for optimum performance. Models trained on training data that isn't
 * representative of the data being processed are likely to miss entities and extract incorrect
 * entities.
 *
 * <p>The performance of statistical methods of extraction is usually measured with a metric known
 * as the F-measure or F1 score. The F-measure is a value between 0 and 1, which takes into account
 * both the precision (how accurate the entities we've extracted are) and recall (how many of the
 * actual entities did we extract). An F-measure of 1 indicates perfect performance.
 */
// Dstl (c) Crown Copyright 2017
package uk.gov.dstl.baleen.annotators.stats;
