The retrieval of irrelevant documents is often caused by the inherent ambiguity of natural language.
Such documents are called false positives (see Type I error). See also: Precision and recall False-positive problem įull-text searching is likely to retrieve many documents that are not relevant to the intended search question. The trade-off between precision and recall is simple: an increase in precision can lower overall recall, while an increase in recall lowers precision.
Controlled-vocabulary searching also helps alleviate low-precision issues by tagging documents in such a way that ambiguities are eliminated. ĭue to the ambiguities of natural language, full-text-search systems typically includes options like stop words to increase precision and stemming to increase recall. The precision for the example is a very low 1/4, or 25%, since only 1 of the 4 results returned was relevant. In the example only 1 relevant result of 3 possible relevant results was returned, so the recall is a very low ratio of 1/3, or 33%. Of all possible results shown, those that were actually returned by the search are shown on a light-blue background. Relevancy is indicated by the proximity of search results to the center of the inner circle. Red dots represent irrelevant results, and green dots represent relevant results. In the diagram the red and green dots represent the total population of potential search results for a given search. The diagram at right represents a low-precision, low-recall search. Precision is the number of relevant results returned to the total number of results returned.
Recall is the ratio of relevant results returned to all relevant results. Recall measures the quantity of relevant results returned by a search, while precision is the measure of the quality of the results returned. recall tradeoff ĭiagram of a low-precision, low-recall search For example, the words "drives", "drove", and "driven" will be recorded in the index under the single concept word "drive". Some indexers also employ language-specific stemming on the words being indexed. Usually the indexer will ignore stop words (such as "the" and "and") that are both common and insufficiently meaningful to be useful in searching. The indexer will make an entry in the index for each term or word found in a document, and possibly note its relative position within the document. In the search stage, when performing a specific query, only the index is referenced, rather than the text of the original documents. 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). 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. This is what some tools, such as grep, do when searching.
#How do i search touchcopy 12 texts serial
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". Some web search engines, such as AltaVista, employ full-text-search techniques, while others index only a portion of the web pages examined by their indexing systems. Many websites and application programs (such as word processing software) provide full-text-search capabilities. Full-text-searching techniques became common in online bibliographic databases in the 1990s. In a full-text search, a search engine examines all of the words in every stored document as it tries to match search criteria (for example, text specified by a user). Full-text search is distinguished from searches based on metadata or on parts of the original texts represented in databases (such as titles, abstracts, selected sections, or bibliographical references). In text retrieval, full-text search, sometimes referred to as free-text-search refers to techniques for searching a single computer-stored document or a collection in a full-text database.
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