substring "America" is itself a name. This segmentation problem is formally similar to chunking. The second phase requires choosing an ontology by which to organize categories of things. Temporal expressions and some numerical expressions (e.g., money, percentages, etc.) may also be considered as named entities in the context of the NER task. While some instances of these types are good examples of rigid designators (e.g., the year 2001) there are also many invalid ones (e.g., I take my vacations in “June”). In the first case, the year 2001 refers to the 2001st year of the Gregorian calendar. In the second case, the month June may refer to the month of an undefined year (past June, next June, every June, etc.). It is arguable that the definition of named entity is loosened in such cases for practical reasons. The definition of the term named entity is therefore not strict and often has to be explained in the context in which it is used. Certain hierarchies of named entity types have been proposed in the literature. BBN categories, proposed in 2002, is used for question answering and consists of 29 types and 64 subtypes. Sekine's extended hierarchy, proposed in 2002, is made of 200 subtypes. More recently, in 2011 Ritter used a hierarchy based on common Freebase entity types in ground-breaking experiments on NER over social media text. Formal evaluation To evaluate the quality of an NER system's output, several measures have been defined. The usual measures are called precision, recall, and F1 score. However, several issues remain in just how to calculate those values. These statistical measures work reasonably well for the obvious cases of finding or missing a real entity exactly; and for finding a non-entity. However, NER can fail in many other ways, many of which are arguably "partially correct", and should not be counted as complete success or failures. For example, identifying a real entity, but: with fewer tokens than desired (for example, missing the last token of "John Smith, M.D.") with more tokens than desired (for example, including the first word of "The University of MD") partitioning adjacent entities differently (for example, treating "Smith, Jones Robinson" as 2 vs. 3 entities) assigning it a completely wrong type (for example, calling a personal name an organization) assigning it a related but inexact type (for example, "substance" vs. "drug", or "school" vs. "organization") correctly identifying an entity, when what