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Fashion Designer & Content Writer
Maria Chowdhury

Fashion Designer & Content Writer  


Teaching English English Translation Bengali English 
$12 /hr
India
Content Writing
Manisha Kumari

Content Writing  


Teaching English Teaching Mathematics Article Rewriting 
$10 /hr
India
Translate in spanish
Arsalan Zaidi

Translate in spanish  


Teaching English Spanish 
$3 /hr
India
ESL trainer and educator
Anukriti Sharma

ESL trainer and educator  


Teaching English ESL Teaching English Language 
$3 /hr
India
Training
Deepak Nikose

Training  


Teaching English Academic Editing Radio Broadcasting 
$7 /hr
India
Education and ICT Consultant
Stephen Chinwendu

Education and ICT Consultant  


Teaching English Teaching Programming Teaching Algebra 
$10 /hr
Nigeria
ThePoet
Mithun Gaming

ThePoet  


Teaching English English English Grammar 
$0 /hr
India
Editor/ English proof reader
Mallika

Editor/ English proof reader  


Teaching English English Proofreading Child Counseling 
$4 /hr
India
English language facilitator
Mehak K. Puri

English language facilitator  


Teaching English English English Grammar 
$10 /hr
India
Digital Marketer
Disha Jain

Digital Marketer  


Teaching English English Proofreading English Writers & Translators 
$6 /hr
India
spanish translator
Suyasha

spanish translator  


Teaching Spanish Translation English Spanish Spanish Writers & Translators 
$1 /hr
India
Bachelor in Psychology and Literary and Culture Studies
Sharvari

Bachelor in Psychology and Literary and Culture Studies  


Teaching English English Language Counseling Psychology 
$1 /hr
India
Spanish Teacher/Interpreter/Translator
Arpit Mahanti

Spanish Teacher/Interpreter/Translator  


Teaching Spanish Spanish Spanish Writers & Translators 
$16 /hr
India
Proofreader and Editor
Madison

Proofreader and Editor  


Teaching English Translation Dutch English English Grammar 
$10 /hr
Canada
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Atie

Tutor  


Teaching English Teaching Translation Persian English 
$10 /hr
India
Content writer and proof reader.
Saba Shekh

Content writer and proof reader.  


Teaching English English English Grammar 
$3 /hr
India
Waddah English teacher
Waddah Alhajar

Waddah English teacher  


Teaching English Translation Arabic English English Grammar 
/hr
India
English Teacher
Pranav Janardhan

English Teacher  


Teaching English English Language Data Entry 
$7 /hr
India
Language enthusiast
Vedika Chaturvedi

Language enthusiast  


Teaching Spanish Translation Spanish English Spanish Proofreading 
$21 /hr
India
Assistant Professor
Ruby

Assistant Professor  


Teaching English Content Writing Academic Editing 
$26 /hr
India
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Rian

Romance Writer  


Teaching English Proofreading Editing 
$15 /hr
New Zealand
Foreign Language Expert
Vani Shah

Foreign Language Expert  


Teaching English Translation English Spanish English Grammar 
$70 /hr
India
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Aakash Tanwani

Instrumentation engineer  


Teaching English Teaching Physics Teaching 
$2 /hr
India
English teacher
Konica

English teacher  


Teaching English Singing 
$0 /hr
India
Bachelor of Arts in English and Literature
Salman Nguro

Bachelor of Arts in English and Literature  


Teaching English English Language Literature Review 
$5 /hr
Kenya
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Kangkan Das

Copy Editor  


Teaching English English Grammar English Language 
$2 /hr
India
Passionate towards English Language Teaching and Urdu-English Translation
Zain Mohammad Sulaiman

Passionate towards English Language Teaching and Urdu-English Translation  


Teaching English Translation Urdu English 
/hr
India
Technical writer with nine years of experience in technical documentation, reviewing, and editing.
Srivani Reddy

Technical writer with nine years of experience in technical documentation, reviewing, and editing.  


Teaching English Teaching Mathematics Technical Editing 
$12 /hr
India
Multilingual Translator with Mechanical Engineering
Ashish Landge

Multilingual Translator with Mechanical Engineering  


Teaching English Translation English French Technical Translation 
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India
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Articles Related To Teaching English


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.

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