The
Themes Locator uses built-in functionality of the
Natural Language Processing engine to extract
the theme or topic of a document. This information is then available for custom analysis via script. The
Natural Language Processing engine
takes several seconds per page longer than regular recognition.
The search returns topics that contain terms you enter. If you type more than one term, an OR is assumed, which returns topics where any of the terms are found. Enclose your search terms in quotes for exact-phrase matching.
The search also uses fuzzy matching to account for partial words (such as install and installs). The results appear in order of relevance, based on how many search terms occur per topic. Exact matches are highlighted.
To refine the search, you can use the following operators:
Type + in front of words that must be included in the search or - in front of words to exclude. (Example: user +shortcut –group finds shortcut and user shortcut, but not group or user group.)
Use * as a wildcard for missing characters. The wildcard can be used anywhere in a search term. (Example: inst* finds installation and instructions.)
Type title: at the beginning of the search phrase to look only for topic titles. (Example: title:configuration finds the topic titled “Changing the software configuration.”)
For multi-term searches, you can specify a priority for terms in your search. Follow the term with ^ and a positive number that indicates the weight given that term. A higher number indicates more weight. (Example: shortcut^10 group gives shortcut 10 times the weight as group.)
To use fuzzy searching to account for misspellings, follow the term with ~ and a positive number for the number of corrections to be made. (Example: port~1 matches fort, post, or potr, and other instances where one correction leads to a match.)
Note that operators cannot be used as search terms: + - * : ~ ^ ' "
The
Themes Locator uses built-in functionality of the
Natural Language Processing engine to extract
the theme or topic of a document. This information is then available for custom analysis via script. The
Natural Language Processing engine
takes several seconds per page longer than regular recognition.