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Hi there, I am a Technical Support and Customer Support Representative who specialize in Chat, Email, Remote access and Phone Support. I have a nin...Read More
Howard Villanueva I am an expert in Data Entry Job. , Philippines
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I have been working for almost 4 years from now and most of my Job is Data Entry Job and IT works. I've known as Expert in Data Entry since most...Read More
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I can type any information and data entry in excel and ms word . Typing is in my blood so don't talk about speed. I rewrite Articles well also.
Babuantony Customer service specialist , India
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8 Years Exp.
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I am a graduate in Bsc zoology, i have 8 years of experience in sales & marketing, customer service and insurance operations.
Aishwarya Your mind reader, India
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I am a employee , with multi talent . i can do all the work as your need. i can be a virtual assistant, word editor, designer and lot of things. I am...Read More
Maria Training Professional, Philippines
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With over eight years of call center experience, handled multiple accounts and exposed to training a class. Teaching and validating requisite skills...Read More
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Professional Experience: Bsc. in Secondary Education Biology and Combined Science- University of The Bahamas Proficiency in Microsoft Office ( W...Read More
Zahith M.writer, India
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assistant Editor Author Communication Specialist Communications Director Communications Manager Content Engineer Content Manager Copy Editor C...Read More
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I am able to type very swiftly as well as use Microsoft office very well. I have been typing for a price for 3 years now.
Marlene Corporate Communications Advisor, Kenya
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I am a skilled individual in a variety of skills.This is followed by the fact that i am a fresh trainee from some of the freelancing basic skills that...Read More
Dinesh K.Epub Specialist, India
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I have a 7 Years of experience in E-pub2, E-pub3, Mobi, PRC, Fixed layout , KF8 , E-pub with Audio Embed and E-pub kids Animated books. All the fil...Read More
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I am certified academic and article writer with a 5-year work experience, working as both ENL and ESL writer. I easily handle assignments and articles...Read More
Kebby student, Kenya
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i am qualified in grammar and writing
Joseph A.Freelancer, Nigeria
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I am a Computer Student of University of the People and I have worked in this industry for quite some time. My background demonstrates a perfect recor...Read More
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I have been working online for 10 years now and have gained various experiences working on administrative and customer service tasks like lead generat...Read More
Business Analyst/Data Analytics professional with 10+ years of relevant work experience in the arena of Business Analysis, Data Visualization, Data An...Read More
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Stephanie V.Customer Service Representative, United States
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Profile Bilingual, self-starter with broad customer service background. Ability to establish priorities and meet challenges head-on. Customer service...Read More
Suniti exprience va &content writer, India
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i am from science department and i have knowledge in few other courses also so i m good at writing , designing , article , power point , copy writing...Read More
Moklesur Trainer, India
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I am a certified office application such as Word, Excel + 8 years experience.
Soma Designer, India
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Skilled designer and data entry professional.Working as a consultant for E commerce company based in Europe.
I am ambitious, self-made, workaholic but down to earth person with a great sense of humor. I like to balance professional and family life. Profession...Read More
Arun S.operation team leader, India
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• Experience spanning around 2+ years reflect proficiencies in Backend Management, Sales, and Business Development. Successfully delivered complex sol...Read More
I am ambitious, self-made, workaholic but down to earth person with a great sense of humor. I like to balance professional and family life. Profession...Read More
Diana T.IT Executive, Malaysia
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I am a professional blogger, article writer, website designer and virtual assistant. Check out my website at www.TSLdesigns.com. I hail from Penang, M...Read More
Gener data entry specialist, Saudi Arabia
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Lately I am working as a clerk in a company in Philippines in almost 10 years. I have a experience in working as clerk and doing data entry, typing, d...Read More
I am certified bioinformatics data analyst having more than 8+ years experience. Have extensive experience in copy editing and writing (broad subject...Read More
Sanghamitra Sahu Online Research work, India
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I can collect data and information from Internet. This is my online research work. I'm Master degree in Computer Science as well as in Engli...Read More
Vishal Z.Everything for your Business, India
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I'm very proficient and have valuable 6+ years of experience in the mentioned list of skills. I have worked in many projects outside of freelance...Read More
Shopify Website Managment Customer Support Services/ ECommerce/ Drop-shipping expert I'm a Hard working and Enthusiastic professional with outsta...Read More
Ahmed R.Data Entry Expert, Pakistan
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I can perform multiple tasks. I just want to do quality work. Winning trust and making relationships is more important than making money for me. I hav...Read More
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NLP is a branch of data science that consists of systematic processes for analyzing, understanding, and deriving information from the text information in a smart and efficient manner. By utilizing NLP and its parts, one can organize the massive chunks of text information, perform various automated tasks and solve a wide range of issues like – automatic summarization, machine translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation etc.

 

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to lexical resources like WordNet, along with a collection of text processing libraries for classification, tokenization, stemming, and tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.

 

NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.”

 

Downloading and installing NLTK

  1. Install NLTK: run pip install nltk
  2. Test installation: run python then type import nltk and run nltk.download() and download all packages.

 

Pre-Processing with NLTK

The main issue with text data is that it's all in text format. However, the Machine learning algorithms need some variety of numerical feature vector so as to perform the task. Thus before we have a tendency to begin with any NLP project we'd like to pre-process it to form it ideal for working. Basic text pre-processing includes:

 

  • Converting the whole text into uppercase or lowercase, in order that the algorithm doesn't treat the same words completely different in several cases.
  • Tokenization: Process of converting the normal text strings into a list of tokens i.e. words that we actually want. The NLTK data package includes a pre-trained Punkt tokenizer for English.

 

           import nltk

           from nltk.tokenize import word_tokenize

           text = "God is Great! I won a lottery."

           print(word_tokenize(text))

           Output: ['God', 'is', 'Great', '!', 'I', 'won', 'a', 'lottery', '.']

 

  • Noise removal: Process of removing everything that isn’t in a standard number or letter.
  • Stop word removal: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”). We would not want these words or taking up valuable processing time. For this, we can remove them easily, by storing a list of words that you consider to be stop words. NLTK (Natural Language Toolkit) in python has a list of stopwords stored in sixteen different languages. You can find them in the nltk_data directory.  home/Saad/nltk_data/corpora/stopwords is the directory address.

           import nltk

           from nltk.corpus import stopwords

           set(stopwords.words('english'))

 

  • Stemming: Stemming is the process of reducing the words to its root form. Example if we were to stem the following words: “Connects”, “Connecting”, “Connected”, “and Connection”, the result would be a single word “Connect”.

           # import these modules

           from nltk.stem import PorterStemmer

           from nltk.tokenize import word_tokenize   

           ps = PorterStemmer()  

           # choose some words to be stemmed

           words = ["Connect", "Connects", “Connected”, "Connecting", "Connection", "Connections"]

 

           for w in words:

           print(w, " : ", ps.stem(w)) 

 

  • Lemmatization: Lemmatization is the process of grouping along the various inflected forms of a word in order that they may be analyzed as a single item. Lemmatization is similar to stemming but it brings context to the words. Therefore it links words with similar meaning to one word.

           # import these modules

           from nltk.stem import WordNetLemmatizer  

           lemmatizer = WordNetLemmatizer()  

           print("rocks :", lemmatizer.lemmatize("rocks"))

           print("corpora :", lemmatizer.lemmatize("corpora"))  

           # a denotes adjective in "pos"

          print("better :", lemmatizer.lemmatize("better", pos ="a"))

 

          -> rocks : rock

          -> corpora : corpus

          -> better : good

 

Now we need to transform text into a meaningful vector array. This vector array is a representation of text that describes the occurrence of words within a document. For example, if our dictionary contains the words {Learning, is, the, not, great}, and we want to vectorize the text “Learning is great”, we would have the following vector: (1, 1, 0, 0, 1). A problem is that extremely frequent words begin to dominate within the document (e.g. larger score), however might not contain as much informational content. Also, it will offer additional weight to longer documents than shorter documents.

 

One approach is to rescale the frequency of words or the scores for frequent words called Term Frequency-Inverse Document Frequency.

 

  • Term Frequency: is a scoring of the frequency of the word in the current document.

           TF = (Number of times term t appears in a document)/ (Number of terms in the document)

 

  • Inverse Document Frequency: It is a scoring of how rare the word is across documents.

           IDF = 1+log(N/n), where, N is the number of documents and n is the number of documents a term t has appeared in.

 

           Tf-idf weight is a weight often used in information retrieval and text mining.

           Tf-IDF can be implemented in scikit learn as:

 

           from sklearn.feature_extraction.text import TfidfVectorizer

           corpus = [

           ...     'This is the first document.’

           ...     'This document is the second document.’

           ...     'And this is the third one.’

           ...     'Is this the first document?',]

           >>> vectorizer = TfidfVectorizer()

           >>> X = vectorizer.fit_transform(corpus)

           >>> print(vectorizer.get_feature_names())

           ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']

           >>> print(X.shape)

           (4, 9)

 

  • Cosine similarity: TF-IDF is a transformation applied to texts to get two real-valued vectors in vector space. We can then obtain the Cosine similarity of any pair of vectors by taking their dot product and dividing that by the product of their norms. That yields the cosine of the angle between the vectors. Cosine similarity is a measure of similarity between two non-zero vectors.

           Cosine Similarity (d1, d2) =  Dot product(d1, d2) / ||d1|| * ||d2||

 

          import numpy as np

          from sklearn.metrics.pairwise import cosine_similarity

          # vectors

          a = np.array([1,2,3])

          b = np.array([1,1,4])

          # manually compute cosine similarity

          dot = np.dot(a, b)

          norma = np.linalg.norm(a)

          normb = np.linalg.norm(b)

          cos = dot / (norma * normb)

 

After completion of cosine similarity matric we perform algorithmic operation on it for Document similarity calculation, sentiment analysis, topic segmentation etc.

 

I have done my best to make the article simple and interesting for you, hope you found it useful and interesting too.

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!!!

Whenever there is a discussion regarding storing information on a 3rd party's database system, questions on security follow. Entrusting another company to stage your valuable information safe is a massive step. Once that information is in your control, you are aware of the protection measures in place to keep it safe.

 

Google assures users that it keeps all information safe and personal unless the user chooses to share files with others. As a part of its security measures, Google does not discuss its approach to security very well. Since users should have a Google account to access Google Docs, and since all accounts need passwords, we all know that at least one stage in Google's security plan depends on password protection.

 

Google Docs is the free data processing software that comes with a Google account. It’s designed to be easy to use. It can be used to create documents with rich formatting, images, and tables and features like footnotes, headers and footers, and page numbering. You can create your documents more engaging with pictures, drawing objects, and tables in Google docs.

 

Why Google Docs is the best way to create blog

If you're a professional blogger, all that you write must obviously be a result of your thorough research and will basically involve hard work. Whether it's Blogspot or WordPress, text editors of each of those blogging platforms are up to notch. Each text editors not only automatically save the post you are writing but also provide sufficient resources for content data formatting that helps you present well your content. Google Docs offers you the easiest and simplest way to format your content, provide blog templates, share it with collaborators, and even upload immediately to whichever CMS you use.

 

Integrate google keeps with google docs

Google Keep has officially been labelled as a part of the Google Suite of tools. It’s currently very easy to keep notes for a document you're working on. Along with the Explore feature, Google Docs has become a seriously impressive tool for business, education, and just about the other purpose that requires note keeping as you write. Google docs provide a tool to integrate google keep notes into document.

 

Migrate google docs to Microsoft word

Google Docs are in a web format, we can’t simply import them into Word! To open Google Docs in Microsoft Word, we need to need to convert Google Docs to Word’s DOCX format, then transfer it afterward. You can easily perform this conversion from Google Docs.

 

Google Docs has been around for a little while now. Businesses are adopting the tool as the way to extend efficiency and usability of information. I have yet to work for a business that actively uses Google Docs on a day to day, however I will definitely see the benefits of google docs.

  1. Accessibility: With Google Docs, staff can access the information 24/7 where they have an internet connection. This kind of flexibility is very useful, particularly for workers who are typically travelling and working from mobile devices.
  2. Version Control: Collaboration have a lot of importance within the workplace. Being able to not only access information from anyplace, but to be able to control the version of any document your staff are working on is a huge asset to your company. Google Docs permits you to add and take away collaborators. You can control exactly who can make changes to the document. In addition, multiple users can access and edit the same document at the same time.
  3. Easy to Learn: Google Docs is very straightforward and easy to pick up. If you have any experience with a word processor or programs such as Word, Excel, etc.
  4. Import/Export Flexibility: Google Docs imports and exports most file types, giving you the flexibility, you need when sending and receiving files from colleagues.

 

Hire Google Docs experts on Toogit.

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