When dealing with a small number of documents it is possible for the full-text search engine to directly scan the contents of the documents with each query, a strategy called serial scanning. This is what some rudimentary tools, such as grep, do when searching.

However, when the number of documents to search is potentially large or the quantity of search queries to perform is substantial the problem of full text search is often divided into two tasks: indexing and searching. The indexing stage will scan the text of all the documents and build a list of search terms, often called an index, but more correctly named a concordance. In the search stage, when performing a specific query, only the index is referenced rather than the text of the original documents.[2]

The indexer will make an entry in the index for each term or word found in a document and possibly its relative position within the document. Usually the indexer will ignore stop words, such as the English "the", which are both too common and carry too little meaning to be useful for searching. Some indexers also employ language-specific stemming on the words being indexed, so for example any of the words "drives", "drove", or "driven" will be recorded in the index under a single concept word "drive".
[edit] The precision vs. recall tradeoff
This diagram represents a low-precision, low-recall search as described in the text.

Recall measures the quantity of results returned by a search and precision is the measure of the quality of the results returned. Recall is the ratio of relevant results returned divided by all relevant results. Precision is the number of relevant results returned divided by the total number of results returned.

The diagram at right represents a low-precision, low-recall search. In the diagram the red and green dots represent the total population of potential search results for a given search. Red dots represent irrelevant results, and green dots represent relevant results. Relevancy is indicated by the proximity of search results to the center of the inner circle. Of all possible results shown, those that were actually returned by the search are shown on a light-blue background. In the example only one relevant result of three possible relevant results was returned, so the recall is a very low ratio of 1:3 or 33%. The precision for the example is a very low 1:4 or 25%, since only one of the four results returned was relevant.[3]

Due to the ambiguities of natural language, full text search systems typically includes options like stop words to increase precision and stemming to increase recall. Controlled-vocabulary searching also helps alleviate low-precision issues by tagging documents in such a way that ambiguities are eliminated. The trade-off between precision and recall is simple: an increase in precision can lower overall recall while an increase in recall lowers precisio