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><DIV
CLASS="SECT1"
><H1
CLASS="SECT1"
><A
NAME="TEXTSEARCH-DICTIONARIES"
>12.6. Dictionaries</A
></H1
><P
> Dictionaries are used to eliminate words that should not be considered in a
search (<I
CLASS="FIRSTTERM"
>stop words</I
>), and to <I
CLASS="FIRSTTERM"
>normalize</I
> words so
that different derived forms of the same word will match. A successfully
normalized word is called a <I
CLASS="FIRSTTERM"
>lexeme</I
>. Aside from
improving search quality, normalization and removal of stop words reduce the
size of the <TT
CLASS="TYPE"
>tsvector</TT
> representation of a document, thereby
improving performance. Normalization does not always have linguistic meaning
and usually depends on application semantics.
</P
><P
> Some examples of normalization:
<P
></P
></P><UL
COMPACT="COMPACT"
><LI
STYLE="list-style-type: disc"
><P
> Linguistic - Ispell dictionaries try to reduce input words to a
normalized form; stemmer dictionaries remove word endings
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> <ACRONYM
CLASS="ACRONYM"
>URL</ACRONYM
> locations can be canonicalized to make
equivalent URLs match:
<P
></P
></P><UL
COMPACT="COMPACT"
><LI
STYLE="list-style-type: disc"
><P
> http://www.pgsql.ru/db/mw/index.html
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> http://www.pgsql.ru/db/mw/
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> http://www.pgsql.ru/db/../db/mw/index.html
</P
></LI
></UL
><P>
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> Color names can be replaced by their hexadecimal values, e.g.,
<TT
CLASS="LITERAL"
>red, green, blue, magenta -> FF0000, 00FF00, 0000FF, FF00FF</TT
>
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> If indexing numbers, we can
remove some fractional digits to reduce the range of possible
numbers, so for example <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>3.14</I
></SPAN
>159265359,
<SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>3.14</I
></SPAN
>15926, <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>3.14</I
></SPAN
> will be the same
after normalization if only two digits are kept after the decimal point.
</P
></LI
></UL
><P>
</P
><P
> A dictionary is a program that accepts a token as
input and returns:
<P
></P
></P><UL
COMPACT="COMPACT"
><LI
STYLE="list-style-type: disc"
><P
> an array of lexemes if the input token is known to the dictionary
(notice that one token can produce more than one lexeme)
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> a single lexeme with the <TT
CLASS="LITERAL"
>TSL_FILTER</TT
> flag set, to replace
the original token with a new token to be passed to subsequent
dictionaries (a dictionary that does this is called a
<I
CLASS="FIRSTTERM"
>filtering dictionary</I
>)
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> an empty array if the dictionary knows the token, but it is a stop word
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="LITERAL"
>NULL</TT
> if the dictionary does not recognize the input token
</P
></LI
></UL
><P>
</P
><P
> <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> provides predefined dictionaries for
many languages. There are also several predefined templates that can be
used to create new dictionaries with custom parameters. Each predefined
dictionary template is described below. If no existing
template is suitable, it is possible to create new ones; see the
<TT
CLASS="FILENAME"
>contrib/</TT
> area of the <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> distribution
for examples.
</P
><P
> A text search configuration binds a parser together with a set of
dictionaries to process the parser's output tokens. For each token
type that the parser can return, a separate list of dictionaries is
specified by the configuration. When a token of that type is found
by the parser, each dictionary in the list is consulted in turn,
until some dictionary recognizes it as a known word. If it is identified
as a stop word, or if no dictionary recognizes the token, it will be
discarded and not indexed or searched for.
Normally, the first dictionary that returns a non-<TT
CLASS="LITERAL"
>NULL</TT
>
output determines the result, and any remaining dictionaries are not
consulted; but a filtering dictionary can replace the given word
with a modified word, which is then passed to subsequent dictionaries.
</P
><P
> The general rule for configuring a list of dictionaries
is to place first the most narrow, most specific dictionary, then the more
general dictionaries, finishing with a very general dictionary, like
a <SPAN
CLASS="APPLICATION"
>Snowball</SPAN
> stemmer or <TT
CLASS="LITERAL"
>simple</TT
>, which
recognizes everything. For example, for an astronomy-specific search
(<TT
CLASS="LITERAL"
>astro_en</TT
> configuration) one could bind token type
<TT
CLASS="TYPE"
>asciiword</TT
> (ASCII word) to a synonym dictionary of astronomical
terms, a general English dictionary and a <SPAN
CLASS="APPLICATION"
>Snowball</SPAN
> English
stemmer:
</P><PRE
CLASS="PROGRAMLISTING"
>ALTER TEXT SEARCH CONFIGURATION astro_en
ADD MAPPING FOR asciiword WITH astrosyn, english_ispell, english_stem;</PRE
><P>
</P
><P
> A filtering dictionary can be placed anywhere in the list, except at the
end where it'd be useless. Filtering dictionaries are useful to partially
normalize words to simplify the task of later dictionaries. For example,
a filtering dictionary could be used to remove accents from accented
letters, as is done by the <A
HREF="unaccent.html"
>unaccent</A
> module.
</P
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-STOPWORDS"
>12.6.1. Stop Words</A
></H2
><P
> Stop words are words that are very common, appear in almost every
document, and have no discrimination value. Therefore, they can be ignored
in the context of full text searching. For example, every English text
contains words like <TT
CLASS="LITERAL"
>a</TT
> and <TT
CLASS="LITERAL"
>the</TT
>, so it is
useless to store them in an index. However, stop words do affect the
positions in <TT
CLASS="TYPE"
>tsvector</TT
>, which in turn affect ranking:
</P><PRE
CLASS="SCREEN"
>SELECT to_tsvector('english','in the list of stop words');
to_tsvector
----------------------------
'list':3 'stop':5 'word':6</PRE
><P>
The missing positions 1,2,4 are because of stop words. Ranks
calculated for documents with and without stop words are quite different:
</P><PRE
CLASS="SCREEN"
>SELECT ts_rank_cd (to_tsvector('english','in the list of stop words'), to_tsquery('list & stop'));
ts_rank_cd
------------
0.05
SELECT ts_rank_cd (to_tsvector('english','list stop words'), to_tsquery('list & stop'));
ts_rank_cd
------------
0.1</PRE
><P>
</P
><P
> It is up to the specific dictionary how it treats stop words. For example,
<TT
CLASS="LITERAL"
>ispell</TT
> dictionaries first normalize words and then
look at the list of stop words, while <TT
CLASS="LITERAL"
>Snowball</TT
> stemmers
first check the list of stop words. The reason for the different
behavior is an attempt to decrease noise.
</P
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-SIMPLE-DICTIONARY"
>12.6.2. Simple Dictionary</A
></H2
><P
> The <TT
CLASS="LITERAL"
>simple</TT
> dictionary template operates by converting the
input token to lower case and checking it against a file of stop words.
If it is found in the file then an empty array is returned, causing
the token to be discarded. If not, the lower-cased form of the word
is returned as the normalized lexeme. Alternatively, the dictionary
can be configured to report non-stop-words as unrecognized, allowing
them to be passed on to the next dictionary in the list.
</P
><P
> Here is an example of a dictionary definition using the <TT
CLASS="LITERAL"
>simple</TT
>
template:
</P><PRE
CLASS="PROGRAMLISTING"
>CREATE TEXT SEARCH DICTIONARY public.simple_dict (
TEMPLATE = pg_catalog.simple,
STOPWORDS = english
);</PRE
><P>
Here, <TT
CLASS="LITERAL"
>english</TT
> is the base name of a file of stop words.
The file's full name will be
<TT
CLASS="FILENAME"
>$SHAREDIR/tsearch_data/english.stop</TT
>,
where <TT
CLASS="LITERAL"
>$SHAREDIR</TT
> means the
<SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> installation's shared-data directory,
often <TT
CLASS="FILENAME"
>/usr/local/share/postgresql</TT
> (use <TT
CLASS="COMMAND"
>pg_config
--sharedir</TT
> to determine it if you're not sure).
The file format is simply a list
of words, one per line. Blank lines and trailing spaces are ignored,
and upper case is folded to lower case, but no other processing is done
on the file contents.
</P
><P
> Now we can test our dictionary:
</P><PRE
CLASS="SCREEN"
>SELECT ts_lexize('public.simple_dict','YeS');
ts_lexize
-----------
{yes}
SELECT ts_lexize('public.simple_dict','The');
ts_lexize
-----------
{}</PRE
><P>
</P
><P
> We can also choose to return <TT
CLASS="LITERAL"
>NULL</TT
>, instead of the lower-cased
word, if it is not found in the stop words file. This behavior is
selected by setting the dictionary's <TT
CLASS="LITERAL"
>Accept</TT
> parameter to
<TT
CLASS="LITERAL"
>false</TT
>. Continuing the example:
</P><PRE
CLASS="SCREEN"
>ALTER TEXT SEARCH DICTIONARY public.simple_dict ( Accept = false );
SELECT ts_lexize('public.simple_dict','YeS');
ts_lexize
-----------
SELECT ts_lexize('public.simple_dict','The');
ts_lexize
-----------
{}</PRE
><P>
</P
><P
> With the default setting of <TT
CLASS="LITERAL"
>Accept</TT
> = <TT
CLASS="LITERAL"
>true</TT
>,
it is only useful to place a <TT
CLASS="LITERAL"
>simple</TT
> dictionary at the end
of a list of dictionaries, since it will never pass on any token to
a following dictionary. Conversely, <TT
CLASS="LITERAL"
>Accept</TT
> = <TT
CLASS="LITERAL"
>false</TT
>
is only useful when there is at least one following dictionary.
</P
><DIV
CLASS="CAUTION"
><P
></P
><TABLE
CLASS="CAUTION"
BORDER="1"
WIDTH="100%"
><TR
><TD
ALIGN="CENTER"
><B
>Caution</B
></TD
></TR
><TR
><TD
ALIGN="LEFT"
><P
> Most types of dictionaries rely on configuration files, such as files of
stop words. These files <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>must</I
></SPAN
> be stored in UTF-8 encoding.
They will be translated to the actual database encoding, if that is
different, when they are read into the server.
</P
></TD
></TR
></TABLE
></DIV
><DIV
CLASS="CAUTION"
><P
></P
><TABLE
CLASS="CAUTION"
BORDER="1"
WIDTH="100%"
><TR
><TD
ALIGN="CENTER"
><B
>Caution</B
></TD
></TR
><TR
><TD
ALIGN="LEFT"
><P
> Normally, a database session will read a dictionary configuration file
only once, when it is first used within the session. If you modify a
configuration file and want to force existing sessions to pick up the
new contents, issue an <TT
CLASS="COMMAND"
>ALTER TEXT SEARCH DICTIONARY</TT
> command
on the dictionary. This can be a <SPAN
CLASS="QUOTE"
>"dummy"</SPAN
> update that doesn't
actually change any parameter values.
</P
></TD
></TR
></TABLE
></DIV
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-SYNONYM-DICTIONARY"
>12.6.3. Synonym Dictionary</A
></H2
><P
> This dictionary template is used to create dictionaries that replace a
word with a synonym. Phrases are not supported (use the thesaurus
template (<A
HREF="textsearch-dictionaries.html#TEXTSEARCH-THESAURUS"
>Section 12.6.4</A
>) for that). A synonym
dictionary can be used to overcome linguistic problems, for example, to
prevent an English stemmer dictionary from reducing the word <SPAN
CLASS="QUOTE"
>"Paris"</SPAN
> to
<SPAN
CLASS="QUOTE"
>"pari"</SPAN
>. It is enough to have a <TT
CLASS="LITERAL"
>Paris paris</TT
> line in the
synonym dictionary and put it before the <TT
CLASS="LITERAL"
>english_stem</TT
>
dictionary. For example:
</P><PRE
CLASS="SCREEN"
>SELECT * FROM ts_debug('english', 'Paris');
alias | description | token | dictionaries | dictionary | lexemes
-----------+-----------------+-------+----------------+--------------+---------
asciiword | Word, all ASCII | Paris | {english_stem} | english_stem | {pari}
CREATE TEXT SEARCH DICTIONARY my_synonym (
TEMPLATE = synonym,
SYNONYMS = my_synonyms
);
ALTER TEXT SEARCH CONFIGURATION english
ALTER MAPPING FOR asciiword
WITH my_synonym, english_stem;
SELECT * FROM ts_debug('english', 'Paris');
alias | description | token | dictionaries | dictionary | lexemes
-----------+-----------------+-------+---------------------------+------------+---------
asciiword | Word, all ASCII | Paris | {my_synonym,english_stem} | my_synonym | {paris}</PRE
><P>
</P
><P
> The only parameter required by the <TT
CLASS="LITERAL"
>synonym</TT
> template is
<TT
CLASS="LITERAL"
>SYNONYMS</TT
>, which is the base name of its configuration file
— <TT
CLASS="LITERAL"
>my_synonyms</TT
> in the above example.
The file's full name will be
<TT
CLASS="FILENAME"
>$SHAREDIR/tsearch_data/my_synonyms.syn</TT
>
(where <TT
CLASS="LITERAL"
>$SHAREDIR</TT
> means the
<SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> installation's shared-data directory).
The file format is just one line
per word to be substituted, with the word followed by its synonym,
separated by white space. Blank lines and trailing spaces are ignored.
</P
><P
> The <TT
CLASS="LITERAL"
>synonym</TT
> template also has an optional parameter
<TT
CLASS="LITERAL"
>CaseSensitive</TT
>, which defaults to <TT
CLASS="LITERAL"
>false</TT
>. When
<TT
CLASS="LITERAL"
>CaseSensitive</TT
> is <TT
CLASS="LITERAL"
>false</TT
>, words in the synonym file
are folded to lower case, as are input tokens. When it is
<TT
CLASS="LITERAL"
>true</TT
>, words and tokens are not folded to lower case,
but are compared as-is.
</P
><P
> An asterisk (<TT
CLASS="LITERAL"
>*</TT
>) can be placed at the end of a synonym
in the configuration file. This indicates that the synonym is a prefix.
The asterisk is ignored when the entry is used in
<CODE
CLASS="FUNCTION"
>to_tsvector()</CODE
>, but when it is used in
<CODE
CLASS="FUNCTION"
>to_tsquery()</CODE
>, the result will be a query item with
the prefix match marker (see
<A
HREF="textsearch-controls.html#TEXTSEARCH-PARSING-QUERIES"
>Section 12.3.2</A
>).
For example, suppose we have these entries in
<TT
CLASS="FILENAME"
>$SHAREDIR/tsearch_data/synonym_sample.syn</TT
>:
</P><PRE
CLASS="PROGRAMLISTING"
>postgres pgsql
postgresql pgsql
postgre pgsql
gogle googl
indices index*</PRE
><P>
Then we will get these results:
</P><PRE
CLASS="SCREEN"
>mydb=# CREATE TEXT SEARCH DICTIONARY syn (template=synonym, synonyms='synonym_sample');
mydb=# SELECT ts_lexize('syn','indices');
ts_lexize
-----------
{index}
(1 row)
mydb=# CREATE TEXT SEARCH CONFIGURATION tst (copy=simple);
mydb=# ALTER TEXT SEARCH CONFIGURATION tst ALTER MAPPING FOR asciiword WITH syn;
mydb=# SELECT to_tsvector('tst','indices');
to_tsvector
-------------
'index':1
(1 row)
mydb=# SELECT to_tsquery('tst','indices');
to_tsquery
------------
'index':*
(1 row)
mydb=# SELECT 'indexes are very useful'::tsvector;
tsvector
---------------------------------
'are' 'indexes' 'useful' 'very'
(1 row)
mydb=# SELECT 'indexes are very useful'::tsvector @@ to_tsquery('tst','indices');
?column?
----------
t
(1 row)</PRE
><P>
</P
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-THESAURUS"
>12.6.4. Thesaurus Dictionary</A
></H2
><P
> A thesaurus dictionary (sometimes abbreviated as <ACRONYM
CLASS="ACRONYM"
>TZ</ACRONYM
>) is
a collection of words that includes information about the relationships
of words and phrases, i.e., broader terms (<ACRONYM
CLASS="ACRONYM"
>BT</ACRONYM
>), narrower
terms (<ACRONYM
CLASS="ACRONYM"
>NT</ACRONYM
>), preferred terms, non-preferred terms, related
terms, etc.
</P
><P
> Basically a thesaurus dictionary replaces all non-preferred terms by one
preferred term and, optionally, preserves the original terms for indexing
as well. <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>'s current implementation of the
thesaurus dictionary is an extension of the synonym dictionary with added
<I
CLASS="FIRSTTERM"
>phrase</I
> support. A thesaurus dictionary requires
a configuration file of the following format:
</P><PRE
CLASS="PROGRAMLISTING"
># this is a comment
sample word(s) : indexed word(s)
more sample word(s) : more indexed word(s)
...</PRE
><P>
where the colon (<TT
CLASS="SYMBOL"
>:</TT
>) symbol acts as a delimiter between a
phrase and its replacement.
</P
><P
> A thesaurus dictionary uses a <I
CLASS="FIRSTTERM"
>subdictionary</I
> (which
is specified in the dictionary's configuration) to normalize the input
text before checking for phrase matches. It is only possible to select one
subdictionary. An error is reported if the subdictionary fails to
recognize a word. In that case, you should remove the use of the word or
teach the subdictionary about it. You can place an asterisk
(<TT
CLASS="SYMBOL"
>*</TT
>) at the beginning of an indexed word to skip applying
the subdictionary to it, but all sample words <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>must</I
></SPAN
> be known
to the subdictionary.
</P
><P
> The thesaurus dictionary chooses the longest match if there are multiple
phrases matching the input, and ties are broken by using the last
definition.
</P
><P
> Specific stop words recognized by the subdictionary cannot be
specified; instead use <TT
CLASS="LITERAL"
>?</TT
> to mark the location where any
stop word can appear. For example, assuming that <TT
CLASS="LITERAL"
>a</TT
> and
<TT
CLASS="LITERAL"
>the</TT
> are stop words according to the subdictionary:
</P><PRE
CLASS="PROGRAMLISTING"
>? one ? two : swsw</PRE
><P>
matches <TT
CLASS="LITERAL"
>a one the two</TT
> and <TT
CLASS="LITERAL"
>the one a two</TT
>;
both would be replaced by <TT
CLASS="LITERAL"
>swsw</TT
>.
</P
><P
> Since a thesaurus dictionary has the capability to recognize phrases it
must remember its state and interact with the parser. A thesaurus dictionary
uses these assignments to check if it should handle the next word or stop
accumulation. The thesaurus dictionary must be configured
carefully. For example, if the thesaurus dictionary is assigned to handle
only the <TT
CLASS="LITERAL"
>asciiword</TT
> token, then a thesaurus dictionary
definition like <TT
CLASS="LITERAL"
>one 7</TT
> will not work since token type
<TT
CLASS="LITERAL"
>uint</TT
> is not assigned to the thesaurus dictionary.
</P
><DIV
CLASS="CAUTION"
><P
></P
><TABLE
CLASS="CAUTION"
BORDER="1"
WIDTH="100%"
><TR
><TD
ALIGN="CENTER"
><B
>Caution</B
></TD
></TR
><TR
><TD
ALIGN="LEFT"
><P
> Thesauruses are used during indexing so any change in the thesaurus
dictionary's parameters <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>requires</I
></SPAN
> reindexing.
For most other dictionary types, small changes such as adding or
removing stopwords does not force reindexing.
</P
></TD
></TR
></TABLE
></DIV
><DIV
CLASS="SECT3"
><H3
CLASS="SECT3"
><A
NAME="TEXTSEARCH-THESAURUS-CONFIG"
>12.6.4.1. Thesaurus Configuration</A
></H3
><P
> To define a new thesaurus dictionary, use the <TT
CLASS="LITERAL"
>thesaurus</TT
>
template. For example:
</P><PRE
CLASS="PROGRAMLISTING"
>CREATE TEXT SEARCH DICTIONARY thesaurus_simple (
TEMPLATE = thesaurus,
DictFile = mythesaurus,
Dictionary = pg_catalog.english_stem
);</PRE
><P>
Here:
<P
></P
></P><UL
COMPACT="COMPACT"
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="LITERAL"
>thesaurus_simple</TT
> is the new dictionary's name
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="LITERAL"
>mythesaurus</TT
> is the base name of the thesaurus
configuration file.
(Its full name will be <TT
CLASS="FILENAME"
>$SHAREDIR/tsearch_data/mythesaurus.ths</TT
>,
where <TT
CLASS="LITERAL"
>$SHAREDIR</TT
> means the installation shared-data
directory.)
</P
></LI
><LI
STYLE="list-style-type: disc"
><P
> <TT
CLASS="LITERAL"
>pg_catalog.english_stem</TT
> is the subdictionary (here,
a Snowball English stemmer) to use for thesaurus normalization.
Notice that the subdictionary will have its own
configuration (for example, stop words), which is not shown here.
</P
></LI
></UL
><P>
Now it is possible to bind the thesaurus dictionary <TT
CLASS="LITERAL"
>thesaurus_simple</TT
>
to the desired token types in a configuration, for example:
</P><PRE
CLASS="PROGRAMLISTING"
>ALTER TEXT SEARCH CONFIGURATION russian
ALTER MAPPING FOR asciiword, asciihword, hword_asciipart
WITH thesaurus_simple;</PRE
><P>
</P
></DIV
><DIV
CLASS="SECT3"
><H3
CLASS="SECT3"
><A
NAME="TEXTSEARCH-THESAURUS-EXAMPLES"
>12.6.4.2. Thesaurus Example</A
></H3
><P
> Consider a simple astronomical thesaurus <TT
CLASS="LITERAL"
>thesaurus_astro</TT
>,
which contains some astronomical word combinations:
</P><PRE
CLASS="PROGRAMLISTING"
>supernovae stars : sn
crab nebulae : crab</PRE
><P>
Below we create a dictionary and bind some token types to
an astronomical thesaurus and English stemmer:
</P><PRE
CLASS="PROGRAMLISTING"
>CREATE TEXT SEARCH DICTIONARY thesaurus_astro (
TEMPLATE = thesaurus,
DictFile = thesaurus_astro,
Dictionary = english_stem
);
ALTER TEXT SEARCH CONFIGURATION russian
ALTER MAPPING FOR asciiword, asciihword, hword_asciipart
WITH thesaurus_astro, english_stem;</PRE
><P>
Now we can see how it works.
<CODE
CLASS="FUNCTION"
>ts_lexize</CODE
> is not very useful for testing a thesaurus,
because it treats its input as a single token. Instead we can use
<CODE
CLASS="FUNCTION"
>plainto_tsquery</CODE
> and <CODE
CLASS="FUNCTION"
>to_tsvector</CODE
>
which will break their input strings into multiple tokens:
</P><PRE
CLASS="SCREEN"
>SELECT plainto_tsquery('supernova star');
plainto_tsquery
-----------------
'sn'
SELECT to_tsvector('supernova star');
to_tsvector
-------------
'sn':1</PRE
><P>
In principle, one can use <CODE
CLASS="FUNCTION"
>to_tsquery</CODE
> if you quote
the argument:
</P><PRE
CLASS="SCREEN"
>SELECT to_tsquery('''supernova star''');
to_tsquery
------------
'sn'</PRE
><P>
Notice that <TT
CLASS="LITERAL"
>supernova star</TT
> matches <TT
CLASS="LITERAL"
>supernovae
stars</TT
> in <TT
CLASS="LITERAL"
>thesaurus_astro</TT
> because we specified
the <TT
CLASS="LITERAL"
>english_stem</TT
> stemmer in the thesaurus definition.
The stemmer removed the <TT
CLASS="LITERAL"
>e</TT
> and <TT
CLASS="LITERAL"
>s</TT
>.
</P
><P
> To index the original phrase as well as the substitute, just include it
in the right-hand part of the definition:
</P><PRE
CLASS="SCREEN"
>supernovae stars : sn supernovae stars
SELECT plainto_tsquery('supernova star');
plainto_tsquery
-----------------------------
'sn' & 'supernova' & 'star'</PRE
><P>
</P
></DIV
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-ISPELL-DICTIONARY"
>12.6.5. <SPAN
CLASS="APPLICATION"
>Ispell</SPAN
> Dictionary</A
></H2
><P
> The <SPAN
CLASS="APPLICATION"
>Ispell</SPAN
> dictionary template supports
<I
CLASS="FIRSTTERM"
>morphological dictionaries</I
>, which can normalize many
different linguistic forms of a word into the same lexeme. For example,
an English <SPAN
CLASS="APPLICATION"
>Ispell</SPAN
> dictionary can match all declensions and
conjugations of the search term <TT
CLASS="LITERAL"
>bank</TT
>, e.g.,
<TT
CLASS="LITERAL"
>banking</TT
>, <TT
CLASS="LITERAL"
>banked</TT
>, <TT
CLASS="LITERAL"
>banks</TT
>,
<TT
CLASS="LITERAL"
>banks'</TT
>, and <TT
CLASS="LITERAL"
>bank's</TT
>.
</P
><P
> The standard <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> distribution does
not include any <SPAN
CLASS="APPLICATION"
>Ispell</SPAN
> configuration files.
Dictionaries for a large number of languages are available from <A
HREF="http://ficus-www.cs.ucla.edu/geoff/ispell.html"
TARGET="_top"
>Ispell</A
>.
Also, some more modern dictionary file formats are supported — <A
HREF="http://en.wikipedia.org/wiki/MySpell"
TARGET="_top"
>MySpell</A
> (OO < 2.0.1)
and <A
HREF="http://sourceforge.net/projects/hunspell/"
TARGET="_top"
>Hunspell</A
>
(OO >= 2.0.2). A large list of dictionaries is available on the <A
HREF="http://wiki.services.openoffice.org/wiki/Dictionaries"
TARGET="_top"
>OpenOffice
Wiki</A
>.
</P
><P
> To create an <SPAN
CLASS="APPLICATION"
>Ispell</SPAN
> dictionary, use the built-in
<TT
CLASS="LITERAL"
>ispell</TT
> template and specify several parameters:
</P
><PRE
CLASS="PROGRAMLISTING"
>CREATE TEXT SEARCH DICTIONARY english_ispell (
TEMPLATE = ispell,
DictFile = english,
AffFile = english,
StopWords = english
);</PRE
><P
> Here, <TT
CLASS="LITERAL"
>DictFile</TT
>, <TT
CLASS="LITERAL"
>AffFile</TT
>, and <TT
CLASS="LITERAL"
>StopWords</TT
>
specify the base names of the dictionary, affixes, and stop-words files.
The stop-words file has the same format explained above for the
<TT
CLASS="LITERAL"
>simple</TT
> dictionary type. The format of the other files is
not specified here but is available from the above-mentioned web sites.
</P
><P
> Ispell dictionaries usually recognize a limited set of words, so they
should be followed by another broader dictionary; for
example, a Snowball dictionary, which recognizes everything.
</P
><P
> Ispell dictionaries support splitting compound words;
a useful feature.
Notice that the affix file should specify a special flag using the
<TT
CLASS="LITERAL"
>compoundwords controlled</TT
> statement that marks dictionary
words that can participate in compound formation:
</P><PRE
CLASS="PROGRAMLISTING"
>compoundwords controlled z</PRE
><P>
Here are some examples for the Norwegian language:
</P><PRE
CLASS="PROGRAMLISTING"
>SELECT ts_lexize('norwegian_ispell', 'overbuljongterningpakkmesterassistent');
{over,buljong,terning,pakk,mester,assistent}
SELECT ts_lexize('norwegian_ispell', 'sjokoladefabrikk');
{sjokoladefabrikk,sjokolade,fabrikk}</PRE
><P>
</P
><DIV
CLASS="NOTE"
><BLOCKQUOTE
CLASS="NOTE"
><P
><B
>Note: </B
> <SPAN
CLASS="APPLICATION"
>MySpell</SPAN
> does not support compound words.
<SPAN
CLASS="APPLICATION"
>Hunspell</SPAN
> has sophisticated support for compound words. At
present, <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> implements only the basic
compound word operations of Hunspell.
</P
></BLOCKQUOTE
></DIV
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="TEXTSEARCH-SNOWBALL-DICTIONARY"
>12.6.6. <SPAN
CLASS="APPLICATION"
>Snowball</SPAN
> Dictionary</A
></H2
><P
> The <SPAN
CLASS="APPLICATION"
>Snowball</SPAN
> dictionary template is based on a project
by Martin Porter, inventor of the popular Porter's stemming algorithm
for the English language. Snowball now provides stemming algorithms for
many languages (see the <A
HREF="http://snowballstem.org/"
TARGET="_top"
>Snowball
site</A
> for more information). Each algorithm understands how to
reduce common variant forms of words to a base, or stem, spelling within
its language. A Snowball dictionary requires a <TT
CLASS="LITERAL"
>language</TT
>
parameter to identify which stemmer to use, and optionally can specify a
<TT
CLASS="LITERAL"
>stopword</TT
> file name that gives a list of words to eliminate.
(<SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>'s standard stopword lists are also
provided by the Snowball project.)
For example, there is a built-in definition equivalent to
</P><PRE
CLASS="PROGRAMLISTING"
>CREATE TEXT SEARCH DICTIONARY english_stem (
TEMPLATE = snowball,
Language = english,
StopWords = english
);</PRE
><P>
The stopword file format is the same as already explained.
</P
><P
> A <SPAN
CLASS="APPLICATION"
>Snowball</SPAN
> dictionary recognizes everything, whether
or not it is able to simplify the word, so it should be placed
at the end of the dictionary list. It is useless to have it
before any other dictionary because a token will never pass through it to
the next dictionary.
</P
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