null+****@clear*****
null+****@clear*****
2011年 12月 28日 (水) 17:14:54 JST
Susumu Yata 2011-12-28 17:14:54 +0900 (Wed, 28 Dec 2011)
New Revision: 990aba80d845723b460a56010e33e9c3860b4199
Log:
[doc] fixed a typo.
Modified files:
doc/locale/ja/LC_MESSAGES/characteristic.po
doc/source/characteristic.txt
Modified: doc/locale/ja/LC_MESSAGES/characteristic.po (+11 -11)
===================================================================
--- doc/locale/ja/LC_MESSAGES/characteristic.po 2011-12-28 17:05:06 +0900 (ad4b22e)
+++ doc/locale/ja/LC_MESSAGES/characteristic.po 2011-12-28 17:14:54 +0900 (77ff141)
@@ -7,7 +7,7 @@ msgid ""
msgstr ""
"Project-Id-Version: 1.2.1\n"
"Report-Msgid-Bugs-To: \n"
-"POT-Creation-Date: 2011-12-28 16:57\n"
+"POT-Creation-Date: 2011-12-28 17:13\n"
"PO-Revision-Date: 2011-11-27 22:53+0900\n"
"Last-Translator: Kouhei Sutou <kou****@clear*****>\n"
"Language-Team: Japanese\n"
@@ -61,8 +61,8 @@ msgstr ""
"groonga の基本機能は C ライブラリとして提供されていますが、MySQL や "
"PostgreSQL と連携させたり、Ruby から呼び出したりすることもできます。そのた"
"め、任意のアプリケーションに組み込むことが可能であり、多様な使い方が考えられ"
-"ます。 興味のある方は `利用例 <http://groonga.org/ja/users/"
-">`_ をご覧ください。"
+"ます。 興味のある方は `利用例 <http://groonga.org/ja/users/>`_ をご覧くださ"
+"い。"
#: ../../../source/characteristic.txt:18
msgid "Full text search and Instant update"
@@ -174,7 +174,7 @@ msgid ""
"tokenizer) and/or a character-based n-gram tokenizer (hereafter referred to "
"as an n-gram tokenizer). A word tokenizer-based search engine is superior in "
"time, space and precision, which is the fraction of relevant documents in a "
-"search result. On the other hand, an n-gram tokenzier-based search engine is "
+"search result. On the other hand, an n-gram tokenizer-based search engine is "
"superior in recall, which is the fraction of retrieved documents in the "
"perfect search result. The best choice depends on the application in "
"practice."
@@ -320,10 +320,10 @@ msgstr ""
"groonga は独自のカラムストアを持つ列指向のデータベースとしての側面を持ってい"
"ますが、既存の RDBMS のストレージエンジンとして利用することもできます。たとえ"
"ば、groonga をベースとする MySQL のストレージエンジンとして `mroonga <http://"
-"mroonga.github.com/ja/>`_ が開発されています。mroonga は MySQL のプラグインとし"
-"て動的にロードすることが可能であり、groonga のカラムストアをストレージとして"
-"利用したり、全文検索エンジンとして groonga を MyISAM や InnoDB と連携させたり"
-"することができます。groonga 単体での利用、およびに MyISAM, InnoDB との連携に"
-"は一長一短があるので、用途に応じて適切な組み合わせを選ぶことが大切です。詳し"
-"くは `関連プロジェクト <http://groonga.org/ja/related-projects.html>`_ を参照"
-"してください。"
+"mroonga.github.com/ja/>`_ が開発されています。mroonga は MySQL のプラグインと"
+"して動的にロードすることが可能であり、groonga のカラムストアをストレージとし"
+"て利用したり、全文検索エンジンとして groonga を MyISAM や InnoDB と連携させた"
+"りすることができます。groonga 単体での利用、およびに MyISAM, InnoDB との連携"
+"には一長一短があるので、用途に応じて適切な組み合わせを選ぶことが大切です。詳"
+"しくは `関連プロジェクト <http://groonga.org/ja/related-projects.html>`_ を参"
+"照してください。"
Modified: doc/source/characteristic.txt (+1 -1)
===================================================================
--- doc/source/characteristic.txt 2011-12-28 17:05:06 +0900 (2ed8bbe)
+++ doc/source/characteristic.txt 2011-12-28 17:14:54 +0900 (0cc9454)
@@ -35,7 +35,7 @@ Inverted index and tokenizer
An inverted index is a traditional data structure used for large-scale full text search. A search engine based on inverted index extracts index terms from a document when it is added. Then in retrieval, a query is divided into index terms to find documents containing those index terms. In this way, index terms play an important role in full text search and thus the way of extracting index terms is a key to a better search engine.
-A tokenizer is a module to extract index terms. A Japanese full text search engine commonly uses a word-based tokenizer (hereafter referred to as a word tokenizer) and/or a character-based n-gram tokenizer (hereafter referred to as an n-gram tokenizer). A word tokenizer-based search engine is superior in time, space and precision, which is the fraction of relevant documents in a search result. On the other hand, an n-gram tokenzier-based search engine is superior in recall, which is the fraction of retrieved documents in the perfect search result. The best choice depends on the application in practice.
+A tokenizer is a module to extract index terms. A Japanese full text search engine commonly uses a word-based tokenizer (hereafter referred to as a word tokenizer) and/or a character-based n-gram tokenizer (hereafter referred to as an n-gram tokenizer). A word tokenizer-based search engine is superior in time, space and precision, which is the fraction of relevant documents in a search result. On the other hand, an n-gram tokenizer-based search engine is superior in recall, which is the fraction of retrieved documents in the perfect search result. The best choice depends on the application in practice.
Groonga supports both word and n-gram tokenizers. The simplest built-in tokenizer uses spaces as word delimiters. Built-in n-gram tokenizers (n = 1, 2, 3) are also available by default. In addition, a yet another built-in word tokenizer is available if MeCab, a part-of-speech and morphological analyzer, is embedded. Note that a tokenizer is pluggable and you can develop your own tokenizer, such as a tokenizer based on another part-of-speech tagger or a named-entity recognizer.