The complete query term is appended to the SQL buffer.
完整的查询项被追加到了sql缓冲区。
This field is mandatory and identifies the query term.
这个字段是强制性的，它识别查询项。
Each full-text query term needs to be interpreted correctly by your query code.
查询代码需要正确地解释每个全文查询词。
It is likely that the query term occurs too frequently relative to the total number of words in the index.
这可能是因为该查询条件相对于索引中的词语总数来说出现的频率太高了。
Listing 1 illustrates how to search Identi.ca for posts matching a specific query term and display them using Zend_Feed.
清单1说明了如何搜索Identi . ca来查找与指定查询条件相匹配的帖子，并且用Zend_Feed来显示。
To demonstrate the patterns in the catalog, I use an aspect that implements search-term highlighting (that is, highlighting a user's query terms in the search results).
为了展示这个编目中的样式，我使用了一个实现突出显示搜索术语(即在搜索结果中突出显示用户的查询术语)的方面。
Abbreviations must be added to a synonym dictionary to be mapped to the full term for index and query processing.
缩写词必须添加到同义词词典中才能映射到索引和查询处理的完整词汇。
Certain characters have semantic meaning as part of the search query, and they need to be escaped if they are intended to be part of a search term.
特定的字符在搜索查询中有特定的语法意义，如果想要作为搜索词汇，那么它们需要进行转义修饰。
Depending on the language that is defined for index creation or the language specified for a query, a term might be expanded in the analysis.
根据创建时定义的语言或查询所指定的语言不同，一个词汇可能会在分析中扩大。
The new version also includes features for indexing and querying including support for term vector, access to scoped analyzer at query time and access to Results Explanation object.
该新版本还包含了索引和查询的一些特性，包括对termvector的支持、在查询期内可以访问范围内的分析器及ResultsExplanation对象。
The following query USES full-text search information to filter those documents having the term "disappointing" in the "message" XML element.
以下查询使用全文搜索信息过滤出在“message”XML元素中有“disappointing”的那些文档。
For example, if you want help on creating a new plug-in, a help search for the term "plug-in" will return cheat sheets — along with other content — that match your query.
例如，如果您需要有关创建新插件的帮助，搜索词汇“plug - in”，Help搜索就会返回一系列同查询相匹配的内容——包括备忘单和其他的内容。
You should also notice that each value term is surrounded by single quotes, exemplified by this full-text query: word='hello'.
还应该注意，每个值由单引号包围，例如全文查询：word='hello'。
Searching can be done via an object model, with the query built up term or term.
我们可以借助于一个对象模型来完成搜索，通过查询来建立条件。
As far as it's concerned, the search term comes from an ordinary form submit process, and it USES that data to complete a query, then return the records that match that term.
它所关注的是只要搜索词来自于一个正常的表单提交过程，它就使用该数据来完成查询，然后返回匹配该搜索词的那些记录。
External optimizer directives are useful when it is not feasible to rewrite a query for a short-term solution to a problem, for example, when a query starts to perform poorly.
在使用改写查询作为问题的短期解决方案不可行的时候，比如查询的执行效率很低的时候，外部优化器指令可能很有用。
Depending on the level of search-term sophistication and the size of the underlying data store, the return set of matches for any particular query may be so large as to be unusable.
根据搜索词汇的复杂度和基础数据仓库的大小，任何特定查询返回的匹配集都可能十分庞大，以致于无法使用。
Longer term, look for Solr to add FieldTypes for better support of sorting without needing to zero out the main query.
从长远看，希望Solr添加FieldType来更好地支持排序，而不需清零主要查询。
For example, Listing 14 shows a query to be or not to be with the focus on the non-filler term not.
例如，清单14显示的一个查询to be or not tobe只关注于非辅助词not。
The simplest and often used projection term is the candidate class of the query itself. It can be implicit, as shown in Listing 11.
最简单并且最常用的预测条件是查询候选类。
You'll make it so that your search term is matched against both the name and phone fields using LIKE, then run the query using mysql_query().
使用 LIKE让搜索词能匹配名字和电话这两个字段，然后使用 mysql_query()运行此查询。
If one did a search for a term contained in the ontology, the query could be expanded with subclass information in order to find more relevant answers.
如果某人对本体中包含的术语进行搜索，查询就可能根据子类的信息进行扩展从而找到更多相关的答案。
Secondly, we apply TWA (term weight Adjustment) algorithm on the TAG to modify the term weights, which, in turn, forms a new query.
其次，利用术语权重调整算法(TWA)修改术语权重，从而形成新的更侧重于目的的查询。
If you review the textbook definition of cosine similarity, you'll find that it's the sum of products of corresponding term weights in a query and a document, normalized.
如果你回顾余弦相似度的教科书的定义，你会发现它是在一个查询和文档的相应术语权重的产品的总和，归一化。
If we want to support accurate ranking, we would need to first execute the query against all shards and gather the relevant term frequencies, and then, based on it, execute the query.
如果我们想支持精确的排名，我们首先需要执行查询频率对所有碎片，收集相关的术语，然后，在此基础上，执行查询。
Data provider (s) failed to execute the search query because a wildcard term matches too many terms for a single information space.
中文原译数据提供者执行搜索查询失败，因为通配符术语与单个信息空间的太多术语匹配。
In this paper, we use the co-occurrence path to explain the relationship between the index words and extract the semantic information in the term-term matrix to expand the query.
本文首先利用传递度来量化索引词与索引词间的关联关系，然后利用索引词与索引词的关系矩阵中存在的语义关系对查询向量进行智能扩展。
It can achieve a long-term online operation, a real-time measurement and analysis, preservation and query of historical data.
它可以实现长期在线运行，实时测量分析，对历史数据进行保存和查询等功能。
It follows that, to compute cosine similarity, you only need to consider those documents that have some term in common with the query.
这表明，计算余弦相似度，你只需要把那些文件，有一些术语通常与查询。
It follows that, to compute cosine similarity, you only need to consider those documents that have some term in common with the query.
这表明，计算余弦相似度，你只需要把那些文件，有一些术语通常与查询。
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