blocks|key|1833816|text|您可以使用WordNet+API来查找单词。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1833817|entityMap|0|LINK|mutability|MUTABLE|url|http://wordnet.princeton.edu/wordnet/related-projects/^0|5|B|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

You can use the <a href="http://wordnet.princeton.edu/wordnet/related-projects/" rel="noreferrer">WordNet API</a> for looking up words.

blocks|key|1087615|text|你需要编译一个停用词列表(一旦你不想在你的搜索中登记)，然后你可以使用那个停用词列表来过滤你的搜索。有关详细信息，您可以考虑查看维基百科上的这些文章|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1087616|1087617|http://en.wikipedia.org/wiki/Stop_words|ordered-list-item|offset|length|1087618|http://en.wikipedia.org/wiki/Natural_language_processing|1087619|1087620|entityMap|0|LINK|mutability|MUTABLE|url|1^0|0|0|0|13|0|0|0|1K|1|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|T|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|U|8|@]|9|@$F|V|G|W|1|X]]|A|$]]|$1|H|3|I|5|E|7|Y|8|@]|9|@$F|Z|G|10|1|11]]|A|$]]|$1|J|3|-4|5|6|7|12|8|@]|9|@]|A|$]]|$1|K|3|-4|5|6|7|13|8|@]|9|@]|A|$]]]|L|$M|$5|N|O|P|A|$Q|D]]|R|$5|N|O|P|A|$Q|I]]]]

You need to compile a list of stop words (once you don't want to enlist in your search) afterwards you can filter your search, using that stop words list. for details you should consider looking at these wikipedia article 

<ol>
<li><a href="http://en.wikipedia.org/wiki/Stop_words" rel="nofollow">http://en.wikipedia.org/wiki/Stop_words</a></li>
<li><a href="http://en.wikipedia.org/wiki/Natural_language_processing" rel="nofollow">http://en.wikipedia.org/wiki/Natural_language_processing</a></li>
</ol>

blocks|key|1087655|text|您可以使用使用字符n元语法统计信息的语言猜测器。通常只需要少量的材料(用于训练和分类)。可以在此处找到文献和实现的链接：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1087656|http://odur.let.rug.nl/~vannoord/TextCat/|offset|length|1087657|方法非常简单：|1087658|1087659|为每个language.|ordered-list-item|1087660|Extract收集少量的文本，并统计文本中出现的1-gram和5-gram。|1087661|根据频率对这些n-gram进行排序，取最好的，比如300。这形成了语言的指纹。|1087662|1087663|如果要对文本或句子进行分类，请应用步骤2和3，并将生成的指纹与训练期间收集的指纹进行比较。根据n-gram的等级差异计算分数，分数最低的语言获胜。|1087664|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|15|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|10|8|@]|9|@$D|11|E|12|1|13]]|A|$]]|$1|F|3|G|5|6|7|14|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|15|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|16|8|@]|9|@]|A|$]]|$1|L|3|M|5|K|7|17|8|@]|9|@]|A|$]]|$1|N|3|O|5|K|7|18|8|@]|9|@]|A|$]]|$1|P|3|-4|5|6|7|19|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1A|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|C]]]]

You can use a language guesser that uses character n-gram statistics. Usually only a small amount of material is needed (both for training and classification). Links to literature and implementations can be found here:

<a href="http://odur.let.rug.nl/~vannoord/TextCat/" rel="nofollow">http://odur.let.rug.nl/~vannoord/TextCat/</a>

The methodology is very simple:

<ol>
<li>Collect a small amount of text for each language.</li>
<li>Extract and count the 1-grams and 5-grams occurring in the text.</li>
<li>Order these n-grams by frequency, taking the best, say 300. This forms the fingerprint of the language.</li>
</ol>

If you want to classify a text or a sentence, you apply steps 2 and 3, and compare the resulting fingerprint to the fingerprints collected during training. Calculate a score based on rank differences of n-grams, the language with the lowest score wins.

blocks|key|1085613|text|您可以使用Python来实现这一点。你要找的是过滤英文单词。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1085614|1085615|首先对句子进行标记化。(将句子拆分成单词)|ordered-list-item|1085616|使用Python+langdetect库查看它是否是英语单词|1085617|根据langdetect输出过滤所有英语单词。|1085618|1085619|如何安装库：|offset|length|style|BOLD|1085620|$+sudo+pip+install+langdetect
Supported+Python+versions+2.6,+2.7,+3.x.

>>>+from+langdetect+import+detect

>>>+detect("War+doesn't+show+who's+right,+just+who's+left.")
'en'
>>>+detect("Ein,+zwei,+drei,+vier")
'de'|code-block|syntax|javascript|1085621|https://pypi.python.org/pypi/langdetect|1085622|附言:不要期望这总是能正常工作：|1085623|>>>+detect("today+is+a+good+day")
'so'
>>>+detect("today+is+a+good+day.")
'so'
>>>+detect("la+vita+e+bella!")
'it'
>>>+detect("khoobi?+khoshi?")
'so'
>>>+detect("wow")
'pl'
>>>+detect("what+a+day")
'en'
>>>+detect("yay!")
'so'|1085624|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|0|0|0|0|0|6|0|0|0|13|0|0|0|0^^$0|@$1|2|3|4|5|6|7|18|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|19|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|1A|8|@]|9|@]|A|$]]|$1|F|3|G|5|E|7|1B|8|@]|9|@]|A|$]]|$1|H|3|I|5|E|7|1C|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|1D|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|1E|8|@$M|1F|N|1G|O|P]]|9|@]|A|$]]|$1|Q|3|R|5|S|7|1H|8|@]|9|@]|A|$T|U]]|$1|V|3|W|5|6|7|1I|8|@]|9|@$M|1J|N|1K|1|1L]]|A|$]]|$1|X|3|Y|5|6|7|1M|8|@]|9|@]|A|$]]|$1|Z|3|10|5|S|7|1N|8|@]|9|@]|A|$T|U]]|$1|11|3|-4|5|6|7|1O|8|@]|9|@]|A|$]]]|12|$13|$5|14|15|16|A|$17|W]]]]

You can use Python for achieving this. What you are looking for is filtering English words. 

<ol>
<li>Firstly tokenize the sentences. (Split the sentences into words) </li>
<li>Use Python langdetect library to see if it is an English word or not</li>
<li>Filter all the english words based on langdetect output.</li>
</ol>

How to install the library:

<pre><code>$ sudo pip install langdetect
Supported Python versions 2.6, 2.7, 3.x.

&gt;&gt;&gt; from langdetect import detect

&gt;&gt;&gt; detect("War doesn't show who's right, just who's left.")
'en'
&gt;&gt;&gt; detect("Ein, zwei, drei, vier")
'de'
</code></pre>

<a href="https://pypi.python.org/pypi/langdetect" rel="nofollow noreferrer">https://pypi.python.org/pypi/langdetect</a>?

P.S.: Don't expect this to work correctly always:

<pre><code>&gt;&gt;&gt; detect("today is a good day")
'so'
&gt;&gt;&gt; detect("today is a good day.")
'so'
&gt;&gt;&gt; detect("la vita e bella!")
'it'
&gt;&gt;&gt; detect("khoobi? khoshi?")
'so'
&gt;&gt;&gt; detect("wow")
'pl'
&gt;&gt;&gt; detect("what a day")
'en'
&gt;&gt;&gt; detect("yay!")
'so'
</code></pre>

blocks|key|1087691|text|package+com;

import+java.io.File;
import+java.io.FileInputStream;
import+java.io.InputStream;

import+edu.cmu.sphinx.api.Configuration;
import+edu.cmu.sphinx.api.SpeechResult;
import+edu.cmu.sphinx.api.StreamSpeechRecognizer;

public+class+TranscriberDemo+{+++++++

++++public+static+void+main(String[]+args)+throws+Exception+{

++++++++Configuration+configuration+=+new+Configuration();

++++++++configuration.setAcousticModelPath("en-us");
++++++++configuration.setDictionaryPath("Sample+Dict+File_2.dic");
++++++++configuration.setLanguageModelPath("Sample+Language+Modeller_2.lm");

++++++++//configuration.setAcousticModelPath("resource:/edu/cmu/sphinx/models/en-us/en-us");
++++++++//configuration.setDictionaryPath("resource:/edu/cmu/sphinx/models/en-us/cmudict-en-us.dict");
++++++++//configuration.setLanguageModelPath("resource:/edu/cmu/sphinx/models/language/en-us.lm.dmp");

++++StreamSpeechRecognizer+recognizer+=+new+StreamSpeechRecognizer(configuration);
++++InputStream+stream+=+new+FileInputStream(new+File("test.wav"));

++++++++recognizer.startRecognition(stream);
++++SpeechResult+result;
++++++++while+((result+=+recognizer.getResult())+!=+null)+{
++++++++System.out.format("Hypothesis:+%25s\n",+result.getHypothesis());
++++}
++++recognizer.stopRecognition();
++++}
}|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|1087692|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>package com;

import java.io.File;
import java.io.FileInputStream;
import java.io.InputStream;

import edu.cmu.sphinx.api.Configuration;
import edu.cmu.sphinx.api.SpeechResult;
import edu.cmu.sphinx.api.StreamSpeechRecognizer;

public class TranscriberDemo { 

 public static void main(String[] args) throws Exception {

 Configuration configuration = new Configuration();

 configuration.setAcousticModelPath("en-us");
 configuration.setDictionaryPath("Sample Dict File_2.dic");
 configuration.setLanguageModelPath("Sample Language Modeller_2.lm");

 //configuration.setAcousticModelPath("resource:/edu/cmu/sphinx/models/en-us/en-us");
 //configuration.setDictionaryPath("resource:/edu/cmu/sphinx/models/en-us/cmudict-en-us.dict");
 //configuration.setLanguageModelPath("resource:/edu/cmu/sphinx/models/language/en-us.lm.dmp");

 StreamSpeechRecognizer recognizer = new StreamSpeechRecognizer(configuration);
 InputStream stream = new FileInputStream(new File("test.wav"));

 recognizer.startRecognition(stream);
 SpeechResult result;
 while ((result = recognizer.getResult()) != null) {
 System.out.format("Hypothesis: %s\n", result.getHypothesis());
 }
 recognizer.stopRecognition();
 }
}
</code></pre>

I have thousands of sentences in a file. I want to find only right/useful English Language words. Is it possible with Natural Language Processing?

Sample Sentence:

~@^.^@~ tic but sometimes world good famous tac Zorooooooooooo

I just want to extract only English Words like

tic world good famous 

Any Advice how can I achieve this. Thanks in Advance

Natural Language Processing

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我的档案里有几千个句子。我只想找到正确的/有用的英语单词。是否可以使用自然语言处理？例句：~@^.^@~ tic，但有时世界上很有名的tac我只想提取像这样的英文单词tic世界好有名气任何建议我如何才能做到这一点。提前感谢

问自然语言处理
EN

回答 5

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问自然语言处理EN

回答 5

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