PostgreSQL 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 contrib/ area of the PostgreSQL distribution for examples.
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-NULL 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.
's Thesaurus - A Dictionary Of Synonyms
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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 Snowball stemmer or simple, which recognizes everything. For example, for an astronomy-specific search (astro_en configuration) one could bind token type asciiword (ASCII word) to a synonym dictionary of astronomical terms, a general English dictionary and a Snowball English stemmer:
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 unaccent module.
It is up to the specific dictionary how it treats stop words. For example, ispell dictionaries first normalize words and then look at the list of stop words, while Snowball stemmers first check the list of stop words. The reason for the different behavior is an attempt to decrease noise.
The simple 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.
We can also choose to return NULL, 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 Accept parameter to false. Continuing the example:
With the default setting of Accept = true, it is only useful to place a simple dictionary at the end of a list of dictionaries, since it will never pass on any token to a following dictionary. Conversely, Accept = false is only useful when there is at least one following dictionary.
A thesaurus dictionary (sometimes abbreviated as TZ) is a collection of words that includes information about the relationships of words and phrases, i.e., broader terms (BT), narrower terms (NT), preferred terms, non-preferred terms, related terms, etc.
Basically a thesaurus dictionary replaces all non-preferred terms by one preferred term and, optionally, preserves the original terms for indexing as well. PostgreSQL's current implementation of the thesaurus dictionary is an extension of the synonym dictionary with added phrase support. A thesaurus dictionary requires a configuration file of the following format:
A thesaurus dictionary uses a subdictionary (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 (*) at the beginning of an indexed word to skip applying the subdictionary to it, but all sample words must be known to the subdictionary.
Specific stop words recognized by the subdictionary cannot be specified; instead use ? to mark the location where any stop word can appear. For example, assuming that a and the are stop words according to the subdictionary:
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 asciiword token, then a thesaurus dictionary definition like one 7 will not work since token type uint is not assigned to the thesaurus dictionary.
Thesauruses are used during indexing so any change in the thesaurus dictionary's parameters requires reindexing. For most other dictionary types, small changes such as adding or removing stopwords does not force reindexing.
pg_catalog.english_stem 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.
Now we can see how it works. ts_lexize is not very useful for testing a thesaurus, because it treats its input as a single token. Instead we can use plainto_tsquery and to_tsvector which will break their input strings into multiple tokens:
The Ispell dictionary template supports morphological dictionaries, which can normalize many different linguistic forms of a word into the same lexeme. For example, an English Ispell dictionary can match all declensions and conjugations of the search term bank, e.g., banking, banked, banks, banks', and bank's.
download dictionary configuration files. OpenOffice extension files have the .oxt extension. It is necessary to extract .aff and .dic files, change extensions to .affix and .dict. For some dictionary files it is also needed to convert characters to the UTF-8 encoding with commands (for example, for a Norwegian language dictionary):
Here, DictFile, AffFile, and StopWords specify the base names of the dictionary, affixes, and stop-words files. The stop-words file has the same format explained above for the simple dictionary type. The format of the other files is not specified here but is available from the above-mentioned web sites.
Ispell dictionaries support splitting compound words; a useful feature. Notice that the affix file should specify a special flag using the compoundwords controlled statement that marks dictionary words that can participate in compound formation:
The Snowball 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 Snowball site 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 language parameter to identify which stemmer to use, and optionally can specify a stopword file name that gives a list of words to eliminate. (PostgreSQL's standard stopword lists are also provided by the Snowball project.) For example, there is a built-in definition equivalent to
A Snowball 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.
In Word 2010 or Word 2013, on the Review tab, click Research. Click Research options in the Research task pane, and then under Reference Books, select the thesaurus options that you want.
In Excel 2010 or Excel 2013, on the Review tab, click Research. Click Research options in the Research task pane, and then under Reference Books, select the thesaurus options that you want.
In Outlook 2010 or Outlook 2013, on the Review tab, click Research. Click Research options in the Research task pane, and then under Reference Books, select the thesaurus options that you want.
In PowerPoint 2010 or PowerPoint 2013, on the Review tab, click Research. Click Research options in the Research task pane, and then under Reference Books, select the thesaurus options that you want.
In Visio 2010 or Visio 2013, on the Review tab, click Research. Click Research options in the Research task pane, and then under Reference Books, select the thesaurus options that you want.
A thesaurus (pl.: thesauri or thesauruses), sometimes called a synonym dictionary or dictionary of synonyms, is a reference work which arranges words by their meanings (or in simpler terms, a book where you can find different words with same meanings to other words),[1][2] sometimes as a hierarchy of broader and narrower terms, sometimes simply as lists of synonyms and antonyms. They are often used by writers to help find the best word to express an idea:
Some thesauri and dictionary synonym notes characterise the distinctions between similar words, with notes on their "connotations and varying shades of meaning".[5] Some synonym dictionaries are primarily concerned with differentiating synonyms by meaning and usage. Usage manuals such as Fowler's Dictionary of Modern English Usage or Garner's Modern English Usage often prescribe appropriate usage of synonyms.
The word "thesaurus" comes from Latin thsaurus, which in turn comes from Greek (thsauros) 'treasure, treasury, storehouse'.[7] The word thsauros is of uncertain etymology.[7][8][9] be457b7860
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