From e438e95c8ccaaba2278b5e30ae296714ecc2d838 Mon Sep 17 00:00:00 2001 From: Lephix Date: Fri, 9 Dec 2016 13:15:53 +0800 Subject: [PATCH] chapter2_part9:/02_Dealing_with_language.asciidoc (#391) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 翻译02_Dealing_with_language.asciidoc * 按照建议进行修改 * 按照建议修改 --- 02_Dealing_with_language.asciidoc | 50 +++++++++++-------------------- 1 file changed, 18 insertions(+), 32 deletions(-) diff --git a/02_Dealing_with_language.asciidoc b/02_Dealing_with_language.asciidoc index de8825aeb..10b83f8d4 100644 --- a/02_Dealing_with_language.asciidoc +++ b/02_Dealing_with_language.asciidoc @@ -1,7 +1,7 @@ ifndef::es_build[= placeholder2] [[languages]] -= Dealing with Human Language += 处理人类语言 [partintro] -- @@ -9,58 +9,44 @@ ifndef::es_build[= placeholder2] ifdef::es_build[] [quote,Matt Groening] ____ -``I know all those words, but that sentence makes no sense to me.'' +``我认识这句话里的所有单词，但并不能理解全句。'' ____ endif::es_build[] ifndef::es_build[] ++++
-

I know all those words, but that sentence makes no sense to me.

+

我认识这句话里的所有单词，但并不能理解全句。

Matt Groening

++++ endif::es_build[] -Full-text search is a battle between _precision_—returning as few -irrelevant documents as possible--and _recall_—returning as many relevant -documents as possible.((("recall", "in full text search")))((("precision", "in full text search")))((("full text search", "battle between precision and recall"))) While matching only the exact words that the user has -queried would be precise, it is not enough. We would miss out on many -documents that the user would consider to be relevant. Instead, we need to -spread the net wider, to also search for words that are not exactly the same -as the original but are related. +全文搜索是一场 _查准率_ 与 _查全率_ 之间的较量—查准率即尽量返回较少的无关文档，而查全率则尽量返回较多的相关文档。 +((("recall", "in full text search")))((("precision", "in full text search")))((("full text search", "battle between precision and recall"))) +尽管能够精准匹配用户查询的单词，但这仍然不够，我们会错过很多被用户认为是相关的文档。 +因此，我们需要把网撒得更广一些，去搜索那些和原文不是完全匹配但却相关的单词。 -Wouldn't you expect a search for ``quick brown fox'' to match a document -containing ``fast brown foxes,'' ``Johnny Walker'' to match ``Johnnie -Walker,'' or ``Arnolt Schwarzenneger'' to match ``Arnold Schwarzenegger''? +难道你不期待在搜索“quick brown fox“时匹配到包含“fast brown foxed“的文档，或是搜索“Johnny Walker“时匹配到“Johnnie Walker“， 又或是搜索“Arnolt Schwarzenneger“时匹配到“Arnold Schwarzenegger“吗？ -If documents exist that _do_ contain exactly what the user has queried, -those documents should appear at the top of the result set, but weaker matches -can be included further down the list. If no documents match -exactly, at least we can show the user potential matches; they may even -be what the user originally intended! +如果文档 _确实_ 包含用户查询的内容，那么这些文档应当出现在返回结果的最前面，而匹配程度较低的文档将会排在靠后的位置。 +如果没有任何完全匹配的文档，我们至少可以给用户展示一些潜在的匹配结果；它们甚至可能就是用户最初想要的结果。 -There are several((("full text search", "finding inexact matches"))) lines of attack: +以下列出了一些可优化的地方：((("full text search", "finding inexact matches"))) -* Remove diacritics like +´+, `^`, and `¨` so that a search for `rôle` will - also match `role`, and vice versa. See <>. +* 清除类似 +´+ ， `^` ， `¨` 的变音符号，这样在搜索 `rôle` 的时候也会匹配 `role` ，反之亦然。请见 <>。 -* Remove the distinction between singular and plural—`fox` versus `foxes`—or between tenses—`jumping` versus `jumped` versus `jumps`—by _stemming_ each word to its root form. See <>. +* 通过提取单词的词干，清除单数和复数之间的差异—`fox` 与 `foxes`—以及时态上的差异—`jumping` 、 `jumped` 与 `jumps` 。请见 <>。 -* Remove commonly used words or _stopwords_ like `the`, `and`, and `or` - to improve search performance. See <>. +* 清除常用词或者 _停用词_ ，如 `the` ， `and` ， 和 `or` ，从而提升搜索性能。请见 <>。 -* Including synonyms so that a query for `quick` could also match `fast`, - or `UK` could match `United Kingdom`. See <>. +* 包含同义词，这样在搜索 `quick` 时也可以匹配 `fast` ，或者在搜索 `UK` 时匹配 `United Kingdom` 。 请见 <>。 -* Check for misspellings or alternate spellings, or match on _homophones_—words that sound the same, like `their` versus `there`, `meat` versus - `meet` versus `mete`. See <>. +* 检查拼写错误和替代拼写方式，或者 _同音异型词_ —发音一致的不同单词，例如 `their` 与 `there` ， `meat` 、 `meet` 与 `mete` 。 请见 <>。 -Before we can manipulate individual words, we need to divide text into -words, ((("words", "dividing text into")))which means that we need to know what constitutes a _word_. We will -tackle this in <>. +在我们可以操控单个单词之前，需要先将文本切分成单词，((("words", "dividing text into")))这也意味着我们需要知道 _单词_ 是由什么组成的。我们将在 <> 章节阐释这个问题。 -But first, let's take a look at how to get started quickly and easily. +在这之前，让我们看看如何更快更简单地开始。 -- include::200_Language_intro.asciidoc[]