Editing Full-text search (section)

==False-positive problem==

Full-text searching is likely to retrieve many documents that are not [[relevance (information retrieval)|relevant]] to the ''intended'' search question. Such documents are called ''false positives'' (see [[Type I and type II errors#Type I error|Type I error]]). The retrieval of irrelevant documents is often caused by the inherent ambiguity of [[natural language]]. In the sample diagram to the right, false positives are represented by the irrelevant results (red dots) that were returned by the search (on a light-blue background).

Clustering techniques based on [[Bayesian inference|Bayesian]] algorithms can help reduce false positives. For a search term of "bank", clustering can be used to categorize the document/data universe into "financial institution", "place to sit", "place to store" etc. Depending on the occurrences of words relevant to the categories, search terms or a search result can be placed in one or more of the categories. This technique is being extensively deployed in the [[Electronic discovery|e-discovery]] domain.{{clarify|date=January 2012}}