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DEVELOPING COLDFUSION 9 APPLICATIONS
Accessing and Using Data
To search for special characters as literals, precede the following nonalphanumeric characters with a backslash
character (\) in a search string:
• comma (,)
• left parenthesis (
• right parenthesis )
• double-quotation mark (")
• backslash (\)
• left curly bracket ({)
• left bracket ([)
• less than sign (<)
• backquote (`)
In addition to the backslash character, you can use paired backquote characters (` `) to interpret special characters as
literals. For example, to search for the wildcard string "a{b" you can surround the string with back quotation marks,
as follows:
`a{b`
To search for a wildcard string that includes the literal backquote character (`) use two backquote characters together
and surround the entire string in back quotation marks:
`*n``t`
You can use paired back quotation marks or backslashes to escape special characters. There is no functional difference
between the two. For example, you can query for the term: <DDA> using
Using natural queries
The Natural parser supports searching for similar documents, a search method sometimes referred to as similarity
searching. The Natural parser supports searching the full text of documents only. The Natural parser does not support
searching collection fields and zones. The Natural parser does not support Verity query language except for topics.
Note: The Verity products and documentation refer to the Natural parser as the Query-By-Example parser, as well as the
Free Text parser.
Meaningful words are automatically treated as if they are preceded by the MANY modifier and the STEM operator.
By implicitly applying the STEM operator, the search engine searches not only for the meaningful words themselves,
but also for words that have the same stem. By implicitly applying the MANY modifier, Verity calculates each
document's score based on the word density it finds for meaningful words; the denser the occurrences of a word in a
document, the higher the document's score.
By default, common words (such as the, has, and for) are stripped away, and the query is built based on the more
significant words (such as personnel, interns, schools, and mentors). Therefore, the results of a natural language search
are likely to be less precise than a search performed using the simple or explicit parser.
The Natural parser interprets topic names as topic objects. This means that if the specified text block contains a topic
name, the query expression represented by the topic is considered in the search.
Last updated 8/5/2010
or
as your search term.
\<DDA\>
`<DDA>`
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