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 | How to use social site for SEO  |
 | As with any online service that starts to get popular traction and experiences rapid growth, social network come micro-blog platform Tumblr has been the target of just about every kind of scam and attempted cyber-criminal subversion out there [inclduing some of its own making](http://www.daniweb.com/internet-marketing/social-media-and-communities/news/459258/tumblr-warns-users-to-change-password-after-security-mess). Most of the time it's not …  |
 | How is your weather in your country? I am living in the Philippines and the weather here today is stormy and according to news, we are overloaded of typhoons in this month. One typhoon is over and there is 2 more waiting on the line. Oh boy!  |
 | Simple enough - you would think. I need Flashplayer in order to watch videos etc. HOWEVER Adobe has (apparently) updated it's product beyond the machine I am currently using - IbookG4 'They' tell us -go to Adobe Archives and install an older version. What they do not tell us is … |
| | I finished my Associates Degree in CIS (Computer Informationn Systems) September 2012. I have not utilized any of my skills and I am in fear that I will completely lose out. I want to go back over my information again and be refreshed, but I would like to have a …  |
 | Suppose there exists a magic medical pill, that increases life expectancy by a tenfold. So ages of 800 and more are easily attainable. Would you like the idea? Say you where born in the year 1000. The English language as we now know it did not even exist. The dark …  |
 | Hello, Another year of victory and pleasure has passed. Happy new year,2014. With every new year, comes better challenges and obstacles in time. May you have a great year and a magnificent time forward. God bless you. |
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 | I have been trying to find the frequency distribution of nouns in a given sentence. If I do this: text = "This ball is blue, small and extraordinary. Like no other ball." token_text= nltk.word_tokenize(text) tagged_sent = nltk.pos_tag(token_text) nouns= [] for word,pos in tagged_sent: if pos in ['NN',"NNP"]: nouns.append(word) freq_nouns=nltk.FreqDist(nouns) print … |