blocks|key|1071687|text|确定文本中单词的适当词性的任务称为Part+of+Speech+Tagging。例如，Brill+tagger混合使用字典(词汇)词和上下文规则。我认为这个任务的一些重要的初始字典单词是停用词。一旦你的单词有了(大部分是正确的)词性，你就可以开始构建更大的结构。This+industry-oriented+book区分了识别名词短语(NP)和识别命名实体。关于教科书：Allen's+Natural+Language+Understanding是一本不错的书，但有点过时。Foundations+of+Statistical+Natural+Language+Processing是对统计自然语言处理的一个很好的介绍。Speech+and+Language+Processing更严格一些，可能也更权威。The+Association+for+Computational+Linguistics是一个领先的计算语言学科学社区。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1071688|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/POS_tagging|1|http://en.wikipedia.org/wiki/Brill_tagger|2|http://books.google.com/books?id=jkkoj7U5g4kC&dq=Natural%2BLanguage%2BProcessing%2Bfor%2BOnline%2BApplications:%2BText%2BRetrieval,%2BExtraction%2Band%2BCategorization&printsec=frontcover&source=bn&hl=en&ei=GVGuSdebCJDRjAf16p2kBg&sa=X&oi=book_result&resnum=7&ct=result#PPP1,M1|3|https://rads.stackoverflow.com/amzn/click/com/0805303340|4|https://rads.stackoverflow.com/amzn/click/com/0262133601|5|https://rads.stackoverflow.com/amzn/click/com/0131873210|6|http://www.aclweb.org/^0|H|M|0|17|C|1|3N|R|2|55|12|3|6L|1I|4|8M|U|5|9T|19|6|0^^$0|@$1|2|3|4|5|6|7|X|8|@]|9|@$A|Y|B|Z|1|10]|$A|11|B|12|1|13]|$A|14|B|15|1|16]|$A|17|B|18|1|19]|$A|1A|B|1B|1|1C]|$A|1D|B|1E|1|1F]|$A|1G|B|1H|1|1I]]|C|$]]|$1|D|3|-4|5|6|7|1J|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]|N|$5|G|H|I|C|$J|O]]|P|$5|G|H|I|C|$J|Q]]|R|$5|G|H|I|C|$J|S]]|T|$5|G|H|I|C|$J|U]]|V|$5|G|H|I|C|$J|W]]]]

The task of determining the proper part of speech for a word in a text is called <a href="http://en.wikipedia.org/wiki/POS_tagging" rel="noreferrer">Part of Speech Tagging</a>. The <a href="http://en.wikipedia.org/wiki/Brill_tagger" rel="noreferrer">Brill tagger</a>, for example, uses a mixture of dictionary(vocabulary) words and contextual rules. I believe that some of the important initial dictionary words for this task are the stop words. 
Once you have (mostly correct) parts of speech for your words, you can start building larger structures. <a href="http://books.google.com/books?id=jkkoj7U5g4kC&amp;dq=Natural+Language+Processing+for+Online+Applications:+Text+Retrieval,+Extraction+and+Categorization&amp;printsec=frontcover&amp;source=bn&amp;hl=en&amp;ei=GVGuSdebCJDRjAf16p2kBg&amp;sa=X&amp;oi=book_result&amp;resnum=7&amp;ct=result#PPP1,M1" rel="noreferrer">This industry-oriented book</a> differentiates between recognizing noun phrases (NPs) and recognizing named entities. 
About textbooks: <a href="https://rads.stackoverflow.com/amzn/click/com/0805303340" rel="noreferrer" rel="nofollow noreferrer">Allen's Natural Language Understanding</a> is a good, but a bit dated, book. <a href="https://rads.stackoverflow.com/amzn/click/com/0262133601" rel="noreferrer" rel="nofollow noreferrer">Foundations of Statistical Natural Language Processing</a> is a nice introduction to statistical NLP. <a href="https://rads.stackoverflow.com/amzn/click/com/0131873210" rel="noreferrer" rel="nofollow noreferrer">Speech and Language Processing</a> is a bit more rigorous and maybe more authoritative. <a href="http://www.aclweb.org/" rel="noreferrer">The Association for Computational Linguistics</a> is a leading scientific community on computational linguistics.

blocks|key|419022|text|尝试搜索“命名实体识别”--这是NLP文献中用来描述这类事情的术语。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|419023|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Try searching for "named entity recognition"--that's the term that's used in the NLP literature for this sort of thing.

blocks|key|418936|text|这取决于你所说的基于字典是什么意思。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|418937|例如，一种策略是取字典中没有的东西，并尝试在假设它们是专有名词的基础上继续。如果这导致了合理的解析，请考虑临时验证的假设并继续进行，否则就得出结论说它们不是。|418938|其他想法：|418939|在主语位置上，任何没有限定词的简单主语在介词短语中都是一个很好的候选者；在任何位置上，所有格限定词的基础(例如candidate.|418940|Ditto+|unordered-list-item|418941|)是一个很好的|418942|418943|
|418944|+|418945|418946|--+MarkusQ|418947|entityMap^0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|X|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|Y|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|Z|8|@]|9|@]|A|$]]|$1|H|3|I|5|J|7|10|8|@]|9|@]|A|$]]|$1|K|3|L|5|J|7|11|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|12|8|@]|9|@]|A|$]]|$1|N|3|O|5|6|7|13|8|@]|9|@]|A|$]]|$1|P|3|Q|5|J|7|14|8|@]|9|@]|A|$]]|$1|R|3|-4|5|6|7|15|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|16|8|@]|9|@]|A|$]]|$1|U|3|-4|5|6|7|17|8|@]|9|@]|A|$]]]|V|$]]

It depends on what you mean by dictionary-based.

For example, one strategy would be to take things that aren't in a dictionary and try to proceed on the assumption that they're proper nouns. If this leads to a sensible parse, consider the assumption provisionally validated and keep going, otherwise conclude that they aren't.

Other ideas:

<ul>
<li>In subject position, any simple subject without a determiner is a good candidate.</li>
<li>Ditto in prepositional phrases</li>
<li>In any position, the basis of a possessive determiner (e.g. Bob in "Bob's sister") is a good candidate </li>
</ul>

-- MarkusQ

blocks|key|3638161|text|一些工具包建议:+1.+Opennlp:你的任务有一个命名实体识别组件2.+LingPipe:还有一个NER组件3.斯坦福NLP包:非常适合学术使用的包，可能对商业不友好。4.nltk:+Python+NLP包|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3638162|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

some toolkits suggested:
1. Opennlp: there is a Named Entity Recognition component for your task
2. LingPipe: also a NER component for it
3. Stanford NLP package: excellent package for academic usage, maybe not commercial friendly.
4. nltk: a Python NLP package

blocks|key|4483720|text|如果你有像“谁是比尔盖茨”这样的句子，如果你对它应用词性标记。它会给出如下答案|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4483721|“谁/WP是/VBZ+bill/NN盖茨/NNS+?/”|4483722|你可以在http://cst.dk/online/pos_tagger/uk/上在线试用|offset|length|4483723|所以你得到的是这句话中所有的名词。现在，您可以使用某种算法轻松地提取这些名词。如果你正在使用自然语言处理，我建议你使用python。它有NLTK(Natural+language+toolkit)，你可以使用它。|4483724|entityMap|0|LINK|mutability|MUTABLE|url|http://cst.dk/online/pos_tagger/uk/^0|0|0|4|Z|0|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$F|U|G|V|1|W]]|A|$]]|$1|H|3|I|5|6|7|X|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]]]

if you have sentence such as "who is bill gates"
And if you apply part of speech tagger to it.
It will give answer as

"who/WP is/VBZ bill/NN gates/NNS ?/. "

U can try this online on 
<a href="http://cst.dk/online/pos_tagger/uk/" rel="nofollow">http://cst.dk/online/pos_tagger/uk/</a>

So you are getting what are all the nouns in this sentence. Now you can easily extract this nouns with some algorithm. I suggest to use python if you are using natural language processing. It has NLTK(Natural language toolkit) with which you can work.

blocks|key|419113|text|如果您对自然语言处理的实现感兴趣，并且python是您的编程语言，那么这将是一个非常有用的资源：http://www.youtube.com/watch?v=kKe4M4iSclc|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|419114|entityMap|0|LINK|mutability|MUTABLE|url|http://www.youtube.com/watch?v=kKe4M4iSclc^0|1C|16|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]]]]

If you're interested in the implementation of natural language processing and python is your programming language, then this can be a very informative resource: <a href="http://www.youtube.com/watch?v=kKe4M4iSclc" rel="nofollow">http://www.youtube.com/watch?v=kKe4M4iSclc</a>

blocks|key|3638273|text|虽然这是为孟加拉语言编写的，但它可以画出一个通用的程序识别专有名词。所以我希望这会对你有所帮助。请查看以下链接：http://www.mecs-press.org/ijmecs/ijmecs-v6-n8/v6n8-1.html|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3638274|entityMap|0|LINK|mutability|MUTABLE|url|http://www.mecs-press.org/ijmecs/ijmecs-v6-n8/v6n8-1.html^0|1K|1L|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]]]]

Though this is for Bengali language, but it can draw a common procedure identified proper noun. So I hope this will be helpful for you.
Please check the folowing link:
<a href="http://www.mecs-press.org/ijmecs/ijmecs-v6-n8/v6n8-1.html" rel="nofollow">http://www.mecs-press.org/ijmecs/ijmecs-v6-n8/v6n8-1.html</a>

I'm interested in learning more about <a href="http://en.wikipedia.org/wiki/Natural_language_processing" rel="noreferrer">Natural Language Processing</a> (NLP) and am curious if there are currently any strategies for recognizing proper nouns in a text that aren't based on dictionary recognition? Also, could anyone explain or link to resources that explain the current dictionary-based methods? Who are the authoritative experts on NLP or what are the definitive resources on the subject?

Strategies for recognizing proper nouns in NLP

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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我有兴趣了解更多关于自然语言处理(  )的知识，并好奇目前是否有任何策略可以识别文本中的专有名词，而不是基于字典识别？另外，有没有人可以解释或链接到解释当前基于字典的方法的资源？谁是自然语言处理方面的权威专家，或者在这个主题上的权威资源是什么？

问自然语言处理中专有名词的识别策略
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回答 7

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