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Maria Chowdhury

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I am a SAP consultant working on multi modules, with certificate of excellence from US and German Clients.
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Camilla Philip

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Yukti Verma

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Saba Shekh

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Bryan Monteiro

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Shilpa Khedekar

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Mary Anne

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Ramesh

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Heli

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Articles Related To English Punctuation


A chatbot is an artificial intelligence powered piece of software in a device, application, web site or alternative networks that try to complete consumer’s needs and then assist them to perform a selected task. Now a days almost every company has a chatbot deployed to interact with the users.

 

Chatbots are often used in many departments, businesses and every environment. They are artificial narrow intelligence (ANI). Chatbots only do a restricted quantity of task i.e. as per their design. However, these Chatbots make our lives easier and convenient. The trend of Chatbots is growing rapidly between businesses and entrepreneurs, and are willing to bring chatbots to their sites. You might also produce it yourself using Python.

 

How do chatbots work?

There are broadly two variants of chatbotsRule-Based and Self learning.

  1. In a Rule-based approach, a bot answers questions based on some rules on that it is trained on. The rules outlined could be very easy to very complicated. The bots will handle easy queries but fail to manage complicated ones.
  2. The Self learning bots are those that use some Machine Learning-based approaches and are positively a lot of economical than rule-based bots. These bots may be of additional two types: Retrieval based or Generative.
    1. In retrieval-based models, Chatbot uses the message and context of conversation for selecting the best response from a predefined list of bot messages.
    2. Generative bots can generate the answers and not always reply with one of the answers from a set of answers. This makes them more intelligent as they take word by word from the query and generates the answers.

 

Building a chatbot using Python

NLP:

The field of study that focuses on the interactions between human language and computers is called Natural Language Processing. NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. However, if you are new to NLP, you can read Natural Language Processing in Python.

 

NLTK:

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use lexical resources such as WordNet, along with a suite of text processing libraries.

 

Importing necessary libraries

import nltk 

import numpy as np 

import random 

import string # to process standard python strings

 

Copy the content in text file named ‘chatbot.txt’, read in the text file and convert the entire file content into a list of sentences and a list of words for further pre-processing.

 

f=open('chatbot.txt','r',errors = 'ignore')

raw=f.read()

raw=raw.lower()# converts to lowercase

nltk.download('punkt') # first-time use only

nltk.download('wordnet') # first-time use only

sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences 

word_tokens = nltk.word_tokenize(raw)# converts to list of words

 

Pre-processing the raw text

We shall now define a function called LemTokens which will take as input the tokens and return normalized tokens.

 

lemmer = nltk.stem.WordNetLemmatizer()

#WordNet is a semantically-oriented dictionary of English included in NLTK.

def LemTokens(tokens):     

return [lemmer.lemmatize(token) for token in tokens]

remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation) 

def LemNormalize(text):     

return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))

 

Keyword matching

Define a function for greeting by bot i.e. if user’s input is greeting, the bot shall return a greeting response.

GREETING_INPUTS = ("hello", "hi", "greetings", "sup", "what's up","hey",)

GREETING_RESPONSES = ["hi", "hey", "*nods*", "hi there", "hello", "I am glad! You are talking to me"]

def greeting(sentence):

for word in sentence.split():

if word.lower() in GREETING_INPUTS:

return random.choice(GREETING_RESPONSES)

 

Generate responses

To generate a response from our bot for input queries, the concept of document similarity is used. Therefore, we start by importing necessary modules.

From scikit learn library, import the TFidf vector to convert a collection of raw documents to a matrix of TF-IDF features

from sklearn.feature_extraction.text import TfidfVectorizer

Also, import cosine similarity module from scikit learn library

from sklearn.metrics.pairwise import cosine_similarity

This will be used to find the similarity between words entered by the user and therefore the words within the corpus. This can be the simplest possible implementation of a chatbot.

Define a function response that searches the user’s vocalization for one or more known keywords and returns one of several possible responses. If it doesn’t find the input matching any of the keywords, it returns a response: “I’m sorry! I don’t understand you”

 

def response(user_response):

robo_response=''

sent_tokens.append(user_response)

TfidfVec = TfidfVectorizer(tokenizer=LemNormalize, stop_words='english')

tfidf = TfidfVec.fit_transform(sent_tokens)

vals = cosine_similarity(tfidf[-1], tfidf)

idx=vals.argsort()[0][-2]

flat = vals.flatten()

flat.sort()

req_tfidf = flat[-2]

if(req_tfidf==0):

robo_response=robo_response+"I am sorry! I don't understand you"

return robo_response

else:  robo_response = robo_response+sent_tokens[idx]

return robo_response

 

I have tried to explain in simple steps how you can build your own chatbot using NLTK and of course it’s not an intelligent one.

I hope you guys have enjoyed reading.

Happy Learning!!!

Python and Java both lay claims to being among the top five most popular programming languages at any given time, with Java usually just ahead of Python. However, Python’s popularity is growing at a tremendous rate, and Python overtake Java in 2018.

 

Python is a high-level, interpreted, interactive and object-oriented scripting language. Python was designed to be highly readable which uses English keywords frequently whereas other languages use punctuation and it has fewer syntactical constructions than other languages.

 

Java and Python have many similarities. Both languages have strong cross-platform support and extensive standard libraries. They both treat (nearly) everything as objects. Both languages compile to bytecode, but Python is (usually) compiled at runtime.

 

Python versus Java:

  1. Java language is more about syntax, if one can forget to add curly braces or semicolon in the end then this will show error as your output. But there is nothing like that with python there is no need of semicolon and curly braces in the end but python follows indentation process so that it will make your code readable.
  2. Java programming is statically typed means that one has to explicitly mention the data type of variable. If datatype (int, float, double, character) isn’t mentioned then the error can occur in program. Python is dynamically typed means if one has directly assigned a value to a variable at the runtime then it'll assume data type.
  3. Java codes are more complex than python codes. Try to write a hello world program in both then you'll observe the complexness of the code, four lines of code in Java and same hello world program in python are of 1-line code.
  4. Python has a large and robust standard library makes Python score over other programming languages. The standard library allows you to choose from a wide range of modules according to your precise needs. Each module further enables you to add functionality to the Python application without writing additional code.
  5. Python is an open source programing language, Python helps you to curtail software system development cost significantly. You’ll even use many open source Python frameworks, libraries and development tools to curtail development time without increasing development cost. You even have choice to select from a wide range of open source Python frameworks and development tools per your precise needs.
  6. Interpreted, with tools like IDLE, you can also interpret Python instead of compiling it. While this reduces the program length, and boosts productivity, it also results in slower overall execution.
  7. Python is considered to be the most favorable language for Machine Learning, Artificial Intelligence, IoT and much more.
  8. Python has a more unified support model than Java for the first time, and open source developers are focusing their efforts on the latest version of the language.
  9. After working on large projects in both languages, I feel secure saying that Python’s syntax is more concise than Java’s. It’s easier to get up and running quickly with a new project in Python than it is in Java.
  10. The most two popular frameworks for Python are Django and Flask. Flask is a micro web framework, it gives you the basic functionalities you’d need like routing requests without much overhead. Django is a more featured option and can help you build a powerful backend while capitalizing on efficiency and security, Django is equipped with a powerful ORM layer which facilitates dealing databases and performing different operations on the data.

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