AngularJS Basics again

Thought of doing basics of AngularJS again and ended up following W3School tutorials which are pretty good. So here is what I learned.

<!doctype html>
<html lang="en">
<head>
  <meta charset="utf-8">
  <title>Back to Basics</title>
  <link rel="stylesheet" href="bower_components/bootstrap/dist/css/bootstrap.css">
  <link rel="stylesheet" href="css/app.css">
  <script src="bower_components/angular/angular.js"></script>

</head>
<body >
<!--ng-app is given in div tag and where the application begins-->
<!--ng-init initialize the application variables-->
<!-- 3rd line ng-init creating the object vehicle-->
<!-- final line is defining an array-->
<div ng-app ng-init="Numone = 3 ; NumTwo = 5 ;
                    Fname = 'KMAN' ; Lname = 'PMAN' ;
                    vehicle = {color : 'red' , wheels : '4'};
                    Lottery = [3,4,34,67,8,3]">
  <p>Adding the two numbers {{Numone + NumTwo}}</p> <!--Adding numbers-->
  <p>The names together {{Fname+" "+Lname}}</p> <!--Binding Strings-->
  <p>The names binding different way <span ng-bind="Fname+' '+ Lname"></span> </p>  <!--Binding Strings using ng-bing-->
  <p>My New vehicle color : <strong> {{vehicle.color}} </strong> and it has &lt,strong>{{vehicle.wheels}}&lt,/strong> no of wheels</p>
  <!--calling the object vehicle-->
  <p>Tonight's lottery numbers are</p>
  <p>{{Lottery[0]+" "+Lottery[3]+" "+Lottery[5]+" "+Lottery[2]}}</p><!--Just some random pick ups from the array-->
</div>
</body>
</html>

So day two done with basics again. Nothing is wasted. Ah by the way the results for above code is :

Natural Language Processing with Python NLTK part 5 - Chunking and Chinking

Natural Language Processing Using regular expression modifiers we can chunk out the PoS tagged words from the earlier example. The chunking is done with regular expressions defining a chunk rule. The Chinking defines what we need to exclude from the selection. Here are list of modifiers for Python: {1,3} = for digits, u expect 1-3 counts of digits, or "places" + = match 1 or more ? = match 0 or 1 repetitions. * = match 0 or MORE repetitions $ = matches at the end of string ^ = matches start of a string | = matches either/or. Example x|y = will match either x or y [] = range, or "variance" {x} = expect to see this amount of the preceding code. {x,y} = expect to see this x-y amounts of the preceding code source: https://pythonprogramming.net/regular-expressions-regex-tutorial-python-3/ Chunking import nltk from nltk.tokenize import word_tokenize # POS tagging sent = "This will be chunked. This is for Test. World is awesome. Hello world....

Natural Language Processing with Python NLTK part 2 - Stop Words

Natural Language Processing Stop words are the words which we ignore due to the fact that they do not generate any specific meaning to the sentence. Words like the, is, at etc. can be removed to extract the meaning of the sentence more easily. So NLTK has introduced us a stop words filter we can easily use. Let's see how it works. from nltk.corpus import stopwords from nltk.tokenize import word_tokenize sent = "As you can see this is the blog of myself which is written by Anjula" w = word_tokenize(sent) # set English stop words stop_words = set(stopwords . words( 'english' )) # list of standard stop words in English print (stop_words) # making empty arrays to store stop words and others stop_words_in_sent = [] non_stop_words = [] # Loop through to get the stop words for x in w: if x not in stop_words: non_stop_words . append(x) else : stop_words_in_sent . append(x) # print result ...

Natural Language Processing with Python NLTK part 6 - Named Entity Recognition

Natural Language Processing - NER Named entities are specific reference to something. As a part of recognizing text NLTK has allowed us to used the named entity recognition and recognize certain types of entities. Those types are as follows NE Type Examples ORGANIZATION Georgia-Pacific Corp. , WHO PERSON Eddy Bonte , President Obama LOCATION Murray River , Mount Everest DATE June , 2008-06-29 TIME two fifty a m , 1:30 p.m. MONEY 175 million Canadian Dollars , GBP 10.40 PERCENT twenty pct , 18.75 % FACILITY Washington Monument , Stonehenge GPE South East Asia , Midlothian Source: http://www.nltk.org/book/ch07.html Simple example on NER: import nltk from nltk.tokenize import word_tokenize, sent_tokenize para = " America is a country. John is a name. " sent = sent_tokenize(para) for s in sent: word = word_tokenize(s) tag = nltk . pos_tag(word) n...

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